Cross Wavelet Transform Research Articles

GRACE based groundwater drought evaluation of Ganga Basin and analysis of drought propagation using wavelet based quantitative approach

The Ganga River Basin which is the home of almost half a billion people have plunged into groundwater drought due to anthropogenic activities. Hence groundwater drought assessment and its propagation from the meteorological drought is highly required for the Ganga River Basin. This paper focuses on the evaluation of historical groundwater drought using Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) dataset, further to obtain the drought propagation times. Traditionally the drought propagation time is obtained from the correlation between groundwater drought time series and various scales of meteorological time series. However, the GRACE-derived groundwater drought index (GGDI) showed lesser correlation with the Standardized Precipitation Index (SPI) / Standardized Precipitation Evapotranspiration Index (SPEI) due to the presence of consistent trend in the GGDI series. Hence, a novel quantitative approach using Cross Wavelet Transform (XWT) is introduced to determine the drought propagation time which can devoid of the contribution of anthropogenic activities. Extracting the significant power area of XWT of GGDI with SPI/SPEI of different scales led to the determination of groundwater drought propagation time. The results showed Groundwater Storage Anomaly (GWSA) has a steep downtrend for Upper Gangetic Basin (UGB) (−26.2 mm/year) and Yamuna Chambal Basin (YCB) (−21.8 mm/year). It was observed that UGB and YCB faced groundwater drought from 2017 to 2022. The wavelet analysis showed that the drought propagation time of YCB is 14 months, UGB is 17 months, and the Lower Gangetic Basin (LGB) is 21 months. The frequency domain analysis of the drought signals suggested YCB had a faster response to the meteorological forcing, and LGB had the slowest response.

Time-frequency co-movements between commodities and global economic policy uncertainty across different crises

Commodity futures constitute an attractive asset class for portfolio managers. Propelled by their low correlation with other assets, commodities begin gaining popularity among investors, as they allow to capture diversification benefits. This comprehensive study examines the time and frequency spillovers between the Economic Policy Uncertainty [1] and a broad set of commodities encompassing ferrous, non-ferrous, and precious metals, food, and energy commodities over a period from December 1997 to April 2022, which includes various political, economic and health crises.The novelty of this research lies in its extensive temporal and categorical coverage, providing an understanding of how different types of commodities respond to various crises. Furthermore, our study breaks new ground by employing wavelet analysis to gain detailed insights in both time and frequency domains in the financial time series of interest, providing a deeper understanding of the co-movements and lead-lag relationships. Specifically, we introduce the Cross Wavelet Transform (XWT) and Wavelet Coherence (WTC) analysis.Our findings demonstrate that not all crises uniformly impact commodities. Notably, during the global financial crisis and the COVID-19 pandemic, co-movements between commodities became significantly stronger. These results highlight the heterogeneity within the commodity asset class, where individual commodities exhibit diverse underlying dynamics. Importantly, the proposed methodology facilitates the extraction of robust results even when dealing with nonlinearities and nonstationary time series data. Consequently, our work offers valuable insights for policymakers (including regulatory bodies), investors, and fund managers.

The possible impact of solar activity on rainfall in Nepal: A case study

This paper investigates the possible relationship between solar activity and monthly rainfall in Pokhara and Kathmandu cities in Nepal from the year 1968 to 2023. The study uses Cross Correlation Analysis (CCA) and wavelet analysis, the Cross-Wavelet Transform (XWT) and Wavelet Transform Coherence (WTC), to analyze the time series data. The CCA shows a strong positive correlation between the solar cycle and rainfall trend, with correlation coefficients of 0.77 and 0.76 and time lags of 18 months and 14 months at Pokhara and Kathmandu, respectively. The p-values, calculated to be < 0.0001 through the t-test, indicate that the observed correlations are statistically significant. The XWT results show a long-term ∼ 11-year common periodicity in solar cycle and rainfall. The WTC analysis confirms the local correlation between these two time series in time–frequency domain at a common periodicity. The results indicate that the monthly rainfall pattern in Nepal is closely related to the solar cycle pattern. Thus, solar activity likely plays a role in influencing rainfall trends in Nepal. This study shows that solar-climate interaction on a regional scale is complex and emphasizes the importance of understanding these relationships for predicting and managing climate change.

Magnetogravimetric study on the Scotia Plate, in the South Atlantic Ocean for the characterization of tsunamis

The marine and coastal environments of the Scotia Sea regions in the Southern Atlantic Ocean and Antarctica are vulnerable to the potentially disastrous effects of seismic activity along the Scotia Arc. This paper presents a magnetogravimetric study of the Scotia Plate for tsunami characterization. The influence of earthquakes on the Geomagnetic Field (GMF) is investigated using data from INTERMAGNET network observatories. A tectonic model is evaluated using gravity data from NOAA and seismic refraction data from Lamont-Doherty Earth Observatory. The study also assesses the impact on water level (WL) measured at Intergovernmental Oceanographic Commission (IOC) tide gauge stations. Cross Wavelet Transform (XWT) is applied, and a frequency analysis of the GMF is conducted to identify specific frequencies during seismic events. A 2D tectonic model is constructed for the North Scotia Ridge using gravimetric and seismic data to characterize structural boundaries that may be activated during seismic events. Water level records collected from 6 tide gauge stations in the region are filtered and analyzed to identify tsunamis at each station. The results reveal anomalous frequencies in the frequency analysis of the horizontal component of the GMF during the November 25, 2013 earthquake, with high data correlation from different observatories in the study area for periods of 0.5 and 1 hour. Gravimetric modeling delineates faults activated during seismic activity and edges of structures potentially activated due to the transcurrent and compressional nature of the margin. WL anomalies up to 1.30 m are obtained following earthquakes with a magnitude greater than 8. The propagation speed in the study area averaged 460 km/h, consistent with the expected speed for those depths, except for Puerto Argentino, which exceeded them in 50%.

Bivariate Assessment of Hydrological Drought of a Semi-Arid Basin and Investigation of Drought Propagation Using a Novel Cross Wavelet Transform Based Technique

Bivariate Assessment of Hydrological Drought of a Semi-Arid Basin and Investigation of Drought Propagation Using a Novel Cross Wavelet Transform Based Technique

Robust Low Complexity Framework for Early Diagnosis of Autism Spectrum Disorder Based on Cross Wavelet Transform and Deep Transfer Learning

Robust Low Complexity Framework for Early Diagnosis of Autism Spectrum Disorder Based on Cross Wavelet Transform and Deep Transfer Learning

Influence of the rainfall and temperature oscillation on air quality in Kathmandu valley: The wavelet analysis

In this study, wavelet analysis and traditional cross-correlation analysis (TCA) have been employed to explore the temporal characteristics of the Air Quality Index (AQI) (PM2.5) and its relationships with meteorological factors, air temperature, and rainfall in the capital city of Nepal, Kathmandu, during 2017 - 2020. The temporal variation of monthly averaged (AQI) (PM&lt;sub&gt;2.5&lt;/sub&gt;) revealed that the air quality was unhealthy in winter, improved in summer, and subsequently became unhealthy again in autumn and spring. We also noticed that the lowest AQI (PM&lt;sub&gt;2.5&lt;/sub&gt;) was recorded in 2020, which is due to the COVID-19 pandemic lockdown. By analyzing the time-frequency relationship of (AQI) (PM&lt;sub&gt;2.5&lt;/sub&gt;) with air temperature and rainfall, using Cross Wavelet Transform (XWT), we observed the interannual, seasonal, and monthly common periodicity between them. The coherency of XWT is then tested by using Wavelet Transform Coherence (WTC), which showed a negative correlation of AQI (PM&lt;sub&gt;2.5&lt;/sub&gt;) with rainfall and air temperature, while an anomaly was seen among AQI (PM&lt;sub&gt;2.5&lt;/sub&gt;) and temperature as they were positively associated for a few months in 2017, 2018, and 2020. Moreover, the results of cross-correlation and multiresolution analysis (MRA) are consistent with wavelet analysis, indicating the driving effect of air temperature and rainfall on (AQI) (PM&lt;sub&gt;2.5&lt;/sub&gt;).

Using Wearable Sensors to Measure Interpersonal Synchrony in Actors and Audience Members During a Live Theatre Performance

Studying social interaction in real-world settings is of increasing importance to social cognitive researchers. Theatre provides an ideal opportunity to study rich face-to-face interactions in a controlled, yet natural setting. Here we collaborated with Flute Theatre to investigate interpersonal synchrony between actors-actors, actors-audience and audience-audience within a live theatrical setting. Our 28 participants consisted of 6 actors and 22 audience members, with 5 of these audience members being audience participants in the show. The performance was a compilation of acting, popular science talks and demonstrations, and an audience participation period. Interpersonal synchrony was measured using inertial measurement unit (IMU) wearable accelerometers worn on the heads of participants, whilst audio-visual data recorded everything that occurred on the stage. Participants also completed post-show self-report questionnaires on their engagement with the overall scientists and actors performance. Cross Wavelet Transform (XWT) and Wavelet Coherence Transform (WCT) analysis were conducted to extract synchrony at different frequencies, pairing with audio-visual data. Findings revealed that XWT and WCT analysis are useful methods in extracting the multiple types of synchronous activity that occurs when people perform or watch a live performance together. We also found that audience members with higher ratings on questionnaire items such as the strength of their emotional response to the performance, or how empowered they felt by the performance, showed a high degree of interpersonal synchrony with actors during the acting segments of performance. We further found that audience members rated the scientists performance higher than the actors performance on questions related to their emotional response to the performance as well as, how uplifted, empowered, and connected to social issues they felt. This shows the types of potent connections audience members can have with live performances. Additionally, our findings highlight the importance of the performance context for audience engagement, in our case a theatre performance as part of public engagement with science rather than a stand-alone theatre performance. In sum we conclude that interdisciplinary real-world paradigms are an important and understudied route to understanding in-person social interactions.

Assessing the changeability of precipitation patterns using multiple remote sensing data and an efficient uncertainty method over different climate regions of Iran

While the rain gauge measurements are unevenly distributed in many world regions, it is necessary to use remote sensing-based precipitation data at high spatial and temporal resolutions. On the other hand, quantifying the uncertainty of precipitation is a vital issue for hydrometeorological applications globally. For this purpose, applying a new fusion framework using high-resolution remote sensing datasets can provide accurate precipitation evaluation against local observations with low uncertainty. This study examines three weighted fusion-based models containing the Ordered-Weighted-Averaged (OWA) family approach based on the ORLIKE method (OWA-ORLIKE) and ORNESS method (OWA-ORNESS) as well as the Entropy-weight (EW) method to combine different remote sensing precipitation products over different climate zones of Iran. In this case, multiple monthly remotely sensed datasets, including ERA5, ERA5-Land, TerraClimate, GPM, PERSIANN-CDR, TRMM, and CHIRPS, are utilized to assess precipitation patterns versus local measurements, which were gathered by Google Earth Engine (GEE) platform. Furthermore, the K-means algorithm is employed to cluster ground-based precipitation stations based on the climate zones category across Iran. Additionally, the Genetic Optimization Algorithm (GOA) is applied to specify optimal values of weights in weighting-based models. The performance of single and combination datasets are evaluated using statistical error metrics, including Pearson correlation coefficient (PCC), root mean square error (RMSE), Kling Gupta efficiency (KGE), and bias. The Thiessen polygon method has been applied to calculate each cluster's mean precipitation to obtain the optimal weights of stations. Finally, as an efficient uncertainty approach, Cross Wavelet Transform (XWT) method has been used for uncertainty assessment of monthly and seasonal precipitation series. Results indicated that the OWA family as the best fusion model had almost the lowest uncertainty values and best statistical error indices compared to the EW model and all single products in different clusters of Iran. As a motivation part of this study, unlike the single sources of remotely sensed data, multiple datasets can apply together in different climate conditions with various features using combination models, which mentioned uncertainty method has clearly revealed this claim.

Mutation Characteristics of Precipitation Concentration Spatiotemporal Variation and Its Potential Correlation with Low-Frequency Climate Factors in the LRB Area from 1960 to 2020

The precipitation conce ntration degree (PCD) and precipitation concentration period (PCP) in the Liaohe River basin (LRB) from 1960 to 2020 were calculated depending on the daily precipitation data derived from meteorological stations. The mutations of the PCD and PCP were identified by sliding t-test, and spatiotemporal evolution characteristics before and after the mutation point were further analyzed. Cross wavelet transform (CWT) was used to reveal the influence of four low-frequency climate factors (Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), El Niño -Southern Oscillation (ENSO), and Sunspots (SS)) on precipitation concentration. The results were presented as follows: Mutations occurred in the PCD sequence in 1980 and the PCP sequence in 2005 in the LRB. Spatial distribution of the PCD generally increased from the southeast to the northwest and tended to flatten. Over the past 60 years, the annual PCD tended to decrease, with a variation range of 0.53 to 0.80. The PCP was relatively concentrated in early July to early August, decreasing before and increasing after the mutation. Important climatic factors driving the mutation of PCD included PDO, SS, and AO. However, the resonance between climate factors and the PCD was characterized by complexity and diversity. The PCP was mainly affected by AO and SS before the mutation. ENSO had an important influence on both PCD and PCP, but had no significant correlation with mutation occurrence.

Revealing the time lag between slope stability and reservoir water fluctuation from InSAR observations and wavelet tools— a case study in Maoergai Reservoir (China)

ABSTRACT Reservoir water fluctuation in supply and storage cycle have strong triggering effects on landslides on both sides of reservoir banks. Early identification of reservoir landslides and revealing the relationship between slope stability and the triggering factors including reservoir level and rainfall, are of great significance in further protecting nearby residents’ lives and properties. In this paper, based on the small baseline subset time series method (SBAS-InSAR), the potential landslides with active displacements in the river bank of Maoergai hydropower station in Heishui County from 2018 to 2020 were monitored with Sentinel-1 data. As a result, a total of 20 unstable slopes were detected. Subsequently, it was found through a gray correlation analysis that the fluctuation of the reservoir water level is the main triggering factor for the displacement on unstable slopes. This paper applied wavelet tools to quantify the time lag between slope stability and reservoir water fluctuation, revealing that the displacement exhibits a seasonal trend, whose high-frequency signal displacement has an interannual period (1 year). Based on the Cross Wavelet Transform (XWT) analysis, under the interannual scale of one year, the reservoir water fluctuation and nonlinear displacement show a clear common power in wavelet. Additionally, a time lag of 65–120 days between slope stability and reservoir water fluctuations has been found, indicating that the non-linear displacements were behind the water level changes. Among the factors affecting the time lag, the elevation of the points and their distance to the bank shore show Pearson’s correlation coefficients of 0.69 and 0.70, respectively. The observed time lag and correlations could be related to the gradual saturation/drainage processes of the slope and the drainage path. This paper demonstrates the technical support to quantitatively reveal the time lag between slope stability and reservoir water fluctuation by InSAR and wavelet tools, providing strong support for the analysis of the mechanisms of landslides in Maoergai reservoir area.

Enhanced Feature Extraction From Assimilated VTCI and LAI With a Particle Filter for Wheat Yield Estimation Using Cross-Wavelet Transform

Enhanced Feature Extraction From Assimilated VTCI and LAI With a Particle Filter for Wheat Yield Estimation Using Cross-Wavelet Transform

Acoustic Diagnosis of Rolling Bearings Fault of CR400 EMU Traction Motor Based on XWT and GoogleNet

Acoustic diagnosis has been a research hotspot in recent years because of the advantages of noncontact signal acquisition. However, acoustic diagnosis technology has not been applied to bearing fault diagnosis of Electric Multiple Units (EMU) traction motor. Traditional fault diagnosis methods are difficult to diagnose acoustic signals with complex noise. An intelligent fault diagnosis method based on Cross Wavelet Transform (XWT) and GoogleNet model is proposed in this paper. Firstly, the fault feature enhancement algorithm is proposed using XWT and bandpass filtering. Secondly, the CR400 EMU traction motor bearing fault test bed is built to collect real fault acoustic signals from two different positions, then XWT is applied to the original signal to identify the fault feature frequency band, then bandpass filtering is used to filter out the noise frequency band other than the fault feature frequency band. Finally, the kurtosis spectrum of the denoised signal and the original signal are input into GoogleNet, respectively, for fault classification. The result shows that (1) GoogleNet achieves 98.23% accuracy in the fault classification for denoised signals, while only 89.66% accuracy for the original signals. (2) Deep learning is an effective method for the acoustic diagnosis of motor bearing faults in EMU trains.

A Comprehensive Analysis of Environmental Loading Effects on Vertical GPS Time Series in Yunnan, Southwest China

Seasonal variations in the vertical Global Positioning System (GPS) time series are mainly caused by environmental loading, e.g., hydrological loading (HYDL), atmospheric loading (ATML), and nontidal oceanic loading (NTOL), which can be synthesized based on models developed by various institutions. A comprehensive comparison among these models is essential to extract reliable vertical deformation data, especially on a regional scale. In this study, we selected 4 HYDL, 5 ATML, 2 NTOL, and their 40 combined products to investigate their effects on seasonal variations in vertical GPS time series at 27 GPS stations in Yunnan, southwest China. These products were provided by the German Research Center for Geosciences (GFZ), School and Observatory of Earth Sciences (EOST), and International Mass Loading Service (IMLS). Furthermore, we used the Cross Wavelet Transform (XWT) method to analyze the relative phase relationship between the GPS and the environmental loading time series. Our result showed that the largest average Root-Mean-Square (RMS) reduction value was 1.32 mm after removing the deformation associated with 4 HYDL from the vertical GPS time series, whereas the RMS reductions after 5 ATML and 2 NTOL model corrections were negative at most stations in Yunnan. The average RMS reduction value of the optimal combination of environmental loading products was 1.24 mm, which was worse than the HYDL (IMLS_GEOSFPIT)-only correction, indicating that HYDL was the main factor responding for seasonal variations at most stations in Yunnan. The XWT result showed that HYDL also explained the annual variations reasonably. Our finding implies that HYDL (IMLS_GEOSFPIT) contributes the most to the environmental loading in Yunnan, and that the ATML and NTOL models used in this paper cannot be effective to correct seasonal variations.

Streamflow frequency changes across western Europe and interactions with North Atlantic atmospheric circulation patterns

This study identifies significant periodicities in streamflow dynamics across western Europe using a hydrological database encompassing 1874 monthly series from catchments in Ireland, the United Kingdom, France, Spain and Portugal, spanning the years 1962 to 2012. Significant and synchronous periodicities with the main atmospheric mechanisms over the North Atlantic sector are also identified using Cross Wavelet Transform and Wavelet Coherence analysis. Principal Components Analysis (PCA) were applied to the different Wavelet transforms analysis in order to summarize the results. These show the occurrence of a 7-years streamflow cycle in a large proportion of catchments within the study domain since the mid 1980's that was not present in earlier periods. The significance, intensity and persistence of the observed regional cycle follows a spatial gradient around the English Channel. We show how the transitive coupling of key atmospheric mechanisms is an influencing factor causing the general change observed. These results suggest the occurrence of a regional change in the periodicities of streamflow across the western European domain. Our results emphasize the non-stationary interaction between streamflow and atmospheric circulation during recent decades and the prominent role of the North Atlantic Oscillation in the newly stablished streamflow cycles. • We assessed the characteristics of streamflow frequencies across Western Europe by means of different Wavelet Transforms. • We used a database encompassing 1874 monthly streamflow series from 1962 to 2012, covering the European Atlantic façade. • The results unveiled the existence of a clear 7-year cycle in a large proportion of the study domain since the mid 1980's. • We show how the transitive coupling of key atmospheric mechanisms is an influencing factor causing the change observed. • The results emphasize the complexity of the non-stationary interaction between streamflow and atmospheric circulation.

A Remotely Sensed Framework for Spatially-Detailed Dryland Soil Organic Matter Mapping: Coupled Cross-Wavelet Transform with Fractional Vegetation and Soil-Related Endmember Time Series

Soil organic matter (SOM) plays pivotal roles in characterizing dryland structure and function; however, remotely sensed spatially-detailed SOM mapping in these regions remains a challenge. Various digital soil mapping approaches based on either single-period remote sensing or spectral indices in other ecosystems usually produce inaccurate, poorly constrained estimates of dryland SOM. Here, a framework for spatially-detailed SOM mapping was proposed based on cross-wavelet transform (XWT) that exploits ecologically meaningful features from intra-annual fractional vegetation and soil-related endmember records. In this framework, paired green vegetation (GV) and soil-related endmembers (i.e., dark surface (DA), saline land (SA), sand land (SL)) sequences were adopted to extract 30 XWT features in temporally and spatially continuous domains of cross-wavelet spectrum. We then selected representative features as exploratory covariates for SOM mapping, integrated with four state-of-the-art machine learning approaches, i.e., ridge regression (RR), least squares-support vector machines (LS-SVM), random forests (RF), and gradient boosted regression trees (GBRT). The results reported that SOM maps from 13 coupled filtered XWT features and four machine learning approaches were consistent with soil-landscape knowledge, as evidenced by a spatially-detailed gradient from oasis to barren. This framework also presented more accurate and reliable results than arithmetically averaged features of intra-annual endmembers and existing datasets. Among the four approaches, both RF and GBRT were more appropriate in the XWT-based framework, showing superior accuracy, robustness, and lower uncertainty. The XWT synthetically characterized soil fertility from the consecutive structure of intra-annual vegetation and soil-related endmember sequences. Therefore, the proposed framework improved the understanding of SOM and land degradation neutrality, potentially leading to more sustainable management of dryland systems.

Bearing fault diagnosis based on XWT-CEEMD noise reduction

In recent years, bearing fault diagnosis has been a research hotspot. In order to improve the reliability of acoustic fault diagnosis, this paper combines Cross Wavelet Transform (XWT) and complementary ensemble empirical mode decomposition (CEEMD) to extract bearing fault features from acoustic signals. Finally, the time-domain features and spectral centroid are input into the SVM for fault classification. The results show that the proposed method can effectively improve the reliability of acoustic fault diagnosis.

Spatiotemporal Variability of Drought and Its Multi-Scale Linkages with Climate Indices in the Huaihe River Basin, Central China and East China

With the aggravation of the ocean–atmosphere cycle anomaly, understanding the potential teleconnections between climate indices and drought/flood conditions can help us know natural hazards more comprehensively to better cope with them. This study aims at exploring the spatiotemporal patterns of drought and its multi-scale relations with typical climate indices in the Huaihe River Basin. First, the spatial patterns were identified based on the seasonal Standardized Precipitation Index (SPI)-3 during 1956–2020 by means of the Empirical Orthogonal Function (EOF). The two leading sub-regions of spring and winter droughts were determined. Then, we extracted the periodicity of spring and winter SPI-3 series and the corresponding seasonal climate indices (Arctic Oscillation (AO), Bivariate El Niño–Southern Oscillation (ENSO)Timeseries (BEST), North Atlantic Oscillation (NAO), Niño3, and Southern Oscillation Index (SOI)) and the sunspot number by using the Continuous Wavelet Transform (CWT). We further explored the teleconnections between spring drought, winter drought, and climate indices and the sunspot number by using Cross Wavelet Transform (XWT) and Wavelet Coherence (WTC) analyses. The results show that there are in-phase multi-scale relations between spring/winter PC1 and AO, BEST, and Niño3, of which the climate indices lead spring PC1 by 1.5–2 years and the climate indices lag winter PC1 by 1.5–3 years. Anti-phase relations between spring PCs and SOI and the sunspot number were observed. NAO mainly affects the interdecadal variation in spring drought, while AO and Niño3 focus on the interannual variation. In addition, Niño3 and SOI are more related to the winter drought on interdecadal scales. Moreover, there is a positive correlation between the monthly average precipitation/temperature and Niño3 with a lag of 3 months. The results are beneficial for improving the accuracy of drought prediction, considering taking NAO, AO, and Niño3 as predictors for spring drought and Niño3 and SOI for winter drought. Hence, valuable information can be provided for the management of water resources as well as early drought warnings in the basin.

Use of Covariance Analysis in Electroencephalogram Reveals Abnormalities in Parkinson’s Disease

Covariance analysis from wavelet data in electroencephalographic records (EEG) was, for the first time, applied in this study to unravel information contained in the standard EEG, which was previously not taken into consideration due to the mathematical models used. The methodology discussed here could be applied to any neurological condition, including the important early stages of neurodegenerative diseases. In this study, we analyzed EEG from control (CL) participants and participants with diagnosed Parkinson’s disease (PD), who were age-matched women in an eyes-closed resting state, to test the model. PD is predicted to rise over the next decades as the population ages. Furthermore, women are more likely to undergo PD-related complications and worse disability than men. Two groups based on age were considered: under and over 60 years (PD patients &lt;60 and &gt;60; CL &lt;60 and &gt;60). Continuous Wavelet Transform and Cross Wavelet Transform were applied to determine patterns of global wavelet curves, main frequencies, and power analyses. Our results indicate that both CL age groups and PD patients &lt;60 share a main α brainwave and PD patients &gt;60 showed a main δ brainwave. Interestingly, power anomalies analyses show a decreasing anteroposterior gradient in CL, whereas it is increasing in PD patients, which was not previously observed. The brainwave power in PD patients &lt;60 was higher in θ, α and β waves and in &gt;60 group, the δ, θ and β brainwaves were predominant. This methodology offers a tool to reveal abnormal electrical brain activity unseen by a regular EEG analysis. The advent of new models that process EEG, such as the model proposed in this study, promotes renewed interest in electrophysiology of the brain to study the early stages of PD and improve understanding of the origin and progress of the disease.

Spatiotemporal changes and modulations of extreme climatic indices in monsoon-dominated climate region linkage with large-scale atmospheric oscillation

Spatiotemporal changes in six precipitation and five temperature extreme indices of Bangladesh and their linkage with nine ocean-atmospheric oscillation indices have been evaluated in this study to provide necessary information for adaptation planning and development of early warning systems. Daily maximum and minimum temperatures and precipitation for the period 1980–2017 recorded at 20 stations, homogeneously distributed over the country, were employed for this purpose. Modified Mann-Kendall (MMK) test was used to evaluate trends in weather extremes, and detrended fluctuation analysis (DFA) was employed to anticipate the possible continuation of existing trends in the future. The cross-wavelet transform (CWT) was used to evaluate the linkage of weather extremes with oscillation indices in the time-frequency domain. The results indicate an increase in hot extremes and a decrease in cool indices in Bangladesh. An increase in the continuous dry day (CDD) and one-day maximum precipitation (RX1day) was also observed, indicating gradual drying and more susceptibility to flash floods at the same time. DFA revealed the possible continuation of existing trends in temperature and precipitation indices. Almost all the climatic extreme indices of Bangladesh were found to follow periodic cycles with different frequencies. The hot extremes were significantly associated with five out of nine oscillation indices, including Atlantic Multidecadal Oscillation (AMO), Arctic Oscillation (AO), East Asian Summer Monsoon Index (EASMI), Sunspot, and South Asian Summer Monsoon Index (SASMI), while cool indices were linked with AMO only. Among the precipitation indices, only CDD was positively related to AO, El Niño Southern Oscillation (ENSO), and Southern Oscillation Index (SOI) and negatively associated with the Pacific Decadal Oscillation (PDO). Analysis of circulation patterns using reanalysis datasets explored that elevated summer geopotential height, no visible anticyclonic center, reduced high cloud cover, and enhanced low cloud covers contributed to increasing hot extremes in Bangladesh.