Explore the vast range of titles available.

This article provides a fresh insight into the dynamic nexus between oil prices, the Saudi/US dollar exchange rate, inflation, and output growth rate in Saudi Arabia' economy, using novel Morlet' wavelet methods. Specifically, it implements various tools of methodology: the continuous wavelet power spectrum, the cross-wavelet power spectrum, the wavelet coherency, the multiple and the partial wavelet coherence to the annual sample period 1969-2014. Our results unveil that the relationships among the variables evolve through time and frequency. From the time-domain view, we show strong but non-homogenous linkages between the four variables. From the frequency-domain view, we uncover significant wavelet coherences and strong lead-lag relationships. From an economic view, the wavelet analysis shows that Saudi economy is still exposed to several global risk factors, which are mainly related to the oil market volatility, and the pegging of the local currency to the US dollar. Such risk factors strongly and negatively affect the real economic growth, exert more pressure on inflation, and substantially limit the freedom to pursue an independent monetary policy.

It is widely accepted that CO2 emissions are the primary cause of climate change and environmental destruction. China, the world’s biggest carbon emitter, is the subject of this research. Utilizing the wavelet tools (wavelet correlation, wavelet coherence, multiple wavelet coherence, and partial wavelet coherence), the present study intends to capture the time-frequency dependence between CO2 emissions and renewable energy, economic growth, trade openness, and energy usage in China between 1965 and 2019. The advantage of the wavelet tools is that they can differentiate between short, medium, and long-run dynamics over the period of study. Furthermore, the study utilized the gradual shift causality test to capture the causal interconnection between CO2 emissions and the regressors. The findings from Bayer and Hanck showed a long-run relationship among the variables of interest. Furthermore, the findings from the wavelet coherence test revealed a positive relationship between CO2 emissions and economic growth and energy usage at all frequencies. Although there is a weak negative relationship between renewable energy and CO2 emissions in the short run, there is no significant co-movement between CO2 emissions and trade openness. The outcomes of the partial and multiple wavelet coherence also give credence to the outcomes of the wavelet coherence test. Lastly, the gradual shift causality test revealed a one-way causality from energy usage and economic growth to CO2 emissions. Based on the findings, suitable policy suggestions were proposed.

The occurrence of extreme precipitation events during Indian Summer Monsoon Rainfall (ISMR) has increased significantly in recent decades. Natural spatio-temporal variability of extreme precipitation events in India has been linked to various climatic variables like El Niño Southern Oscillation (ENSO), Equatorial Indian Ocean Oscillation (EQUINOO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO). In this study, extreme precipitation indices are used to characterize the ISMR extremes and possible individual and coupled association with climatic variables identified using wavelet analysis. Region-based analysis revealed that ENSO, EQUINOO, PDO, and AMO influence extreme precipitation events on spatio-temporal scales. Variability of the duration of extreme precipitation events strongly depends on the ENSO at interannual scale compared to the other climate variables whereas, total precipitation greater than 95th percentile and maximum consecutive 5-day precipitation values were significantly coherent on inter-decadal scale with ENSO, EQUINOO, and PDO. It is also found that the climate variables together cause variability in ISMR extremes, particularly AMO-ENSO-EQUINOO and AMO-ENSO-PDO combinations explain the variability better than any other combination. An increase in the number of climate variables did not improve the coherence, since these climatic variables are correlated with each other. Further, the decomposition of wavelets at different scales shows that more than half of the grid points considered were significant at interdecadal and multidecadal scales even though they are designated with different time scales. This indicates that the non-stationary behavior of the ISMR extremes is directly linked to the climatic variables at higher scales.

There exist many seminal studies in the literature searching the influences of total renewable usage (or usage of its some components) on CO 2 emissions. However, to the best of our knowledge, there does not exist a research work in the literature considering the co-movements between waste energy consumption and CO 2 emissions. This paper aims at observing the possible negative or positive impacts of waste energy consumption on environmental quality (in terms of CO 2 ) by monitoring the whole sample period and all subsample periods in the USA for the monthly period 1980:1–2018:12. This paper searches also the positive or negative lead-lag relations between waste and CO 2 emissions, if exist, by considering high frequency (1–3-year cycle) and low frequency (3–8-year cycle) of the same sample period and subsample periods. The findings obtained by this research in general might underline (a) the outcome indicating that waste energy usage has been effective in diminishing the carbon emissions after the second half of the 2010s in the USA and (b) the energy policy act(s) in the USA implemented which eventually resulted in lower CO 2 emissions in the USA especially after the second half of the 2000s. The paper suggests as well some policy proposals which might result in positive contribution of waste energy on environmental quality.

It is well documented that carbon emissions can be reduced by replacing conventional energy resources with renewable energy resources; thereby, the role of green technology is essential as it protect natural environment. Given that, the United Nations’ agenda of “green is clean” may be achievable by adoption of green technologies. The objective of the study is to examine the link between information and communication technology (ICT), economic growth, energy consumption, and carbon dioxide (CO 2 ) emissions in the context of South Korean economy, by using a novel Morlet wavelet approach. The study applies continuous wavelet power spectrum, the wavelet coherency, and the partial and the multiple wavelet coherency to the year during 1973–2016. The outcomes reveal that the connections among the stated variables progress over frequency and time domain. From the frequency domain point of view, the current study discovers noteworthy wavelet coherence and robust lead and lag linkages. From the time-domain sight, the results display robust but not consistent associations among the considered variables. From an economic point sight, the wavelet method displays that ICT helps to reduce environmental degradation in a medium and long run in the South Korean economy. This emphasizes the significance of having organized strategies by the policymakers to cope up with 2 to 3 years of the occurrence of the huge environmental degradation in South Korea.

Regional weather and climate are generally impacted by global climatic phenomenon′s. Understanding the impact of global climate phenomenon′s on an atmospheric branch of the hydrological cycle is crucial to make advances in skillful precipitation forecast. The present study adopts a multiscale approach based on wavelets for unravelling the linkages between teleconnections and atmospheric moisture transport over homogeneous regions of Indian sub-continent. We investigated linkages between atmospheric moisture transport quantified as monthly integrated water vapor transport (IVT) during 1951–2022 over selected homogeneous regions and eight large scale climate oscillations using wavelet and global wavelet coherence. Our results indicate significant heterogeneity in linkages across different regions and across multiple timescales. In particular, the Indian Ocean Dipole (IOD) influence monthly IVT at intra-annual to inter-annual scale over all regions. The El Niño–Southern Oscillation (ENSO) have strong connection to monthly IVT at inter-annual scale whereas over west central region both IOD and ENSO strongly influence IVT at inter-decadal scale. While the Atlantic Multi-Decadal Oscillation and Pacific Decadal Oscillation have an impact on IVT in the north-east and southern regions, the Arctic Oscillation and North Atlantic oscillation have a strong inter-annual connection to IVT, majorly in the northwest and hilly regions. Overall, the methodology offers an effective approach for capturing the dynamics of atmospheric moisture transport in time–frequency space and provide a practical reference for prediction of atmospheric moisture transport linked precipitation over different regions of Indian subcontinent.

Functional Near-Infrared Spectroscopy (fNIRS) is a promising method to study functional organization of the prefrontal cortex. However, in order to realize the high potential of fNIRS, effective discrimination between physiological noise originating from forehead skin haemodynamic and cerebral signals is required. Main sources of physiological noise are global and local blood flow regulation processes on multiple time scales. The goal of the present study was to identify the main physiological noise contributions in fNIRS forehead signals and to develop a method for physiological de-noising of fNIRS data. To achieve this goal we combined concurrent time-domain fNIRS and peripheral physiology recordings with wavelet coherence analysis (WCA). Depth selectivity was achieved by analyzing moments of photon time-of-flight distributions provided by time-domain fNIRS. Simultaneously, mean arterial blood pressure (MAP), heart rate (HR), and skin blood flow (SBF) on the forehead were recorded. WCA was employed to quantify the impact of physiological processes on fNIRS signals separately for different time scales. We identified three main processes contributing to physiological noise in fNIRS signals on the forehead. The first process with the period of about 3 s is induced by respiration. The second process is highly correlated with time lagged MAP and HR fluctuations with a period of about 10 s often referred as Mayer waves. The third process is local regulation of the facial SBF time locked to the task-evoked fNIRS signals. All processes affect oxygenated haemoglobin concentration more strongly than that of deoxygenated haemoglobin. Based on these results we developed a set of physiological regressors, which were used for physiological de-noising of fNIRS signals. Our results demonstrate that proposed de-noising method can significantly improve the sensitivity of fNIRS to cerebral signals.

Exploring the change and teleconnection of climate on the Tibetan Plateau is of utmost importance to understand global climate change. However, it remains further work because of two aspects: (1) The sparse and short observation provides limited information of climate change. (2) The teleconnection analysis mostly ignores the combined effects of multiple atmospheric circulation indices. This study used a climate dataset with grid size of 0.5′ for the period of 1901–2017 to analyze the spatiotemporal characteristics of precipitation and temperature on the Tibetan Plateau. Further, we employed the multivariate wavelet coherence to investigate the combined effects of multiple circulation indices (Atlantic Multidecadal Oscillation—AMO, El Nino-Southern Oscillation—ENSO, Indian Ocean Dipole Index—IOD, North Atlantic Oscillation—NAO, and North Pacific Oscillation—NPO) on climate at varying scales over time. The long-term averages of precipitation and temperature varied spatially because of the terrain effects. The climate for 1901–2017 was overall getting wetter and warmer, but the interannual variability split the whole study period into three sub-periods: drier and warmer for 1901–1940, wetter and colder for 1941–1966, and wetter and warmer for 1966–2017. For a single atmospheric circulation index, ENSO or NPO had the greatest impacts on the climate change at the scale of 8–16 months over the whole study period. Further, multiple atmospheric circulation index can combine to have simultaneous effects (e.g., NAO–NPO) on climate change. The interannual variability of climate can be attributed to that of a single or combined atmospheric circulation indices. These results provide fundamental information for studies of climate change on the Tibetan Plateau and the globe, and the multivariate wavelet analysis is promising in interpreting the impacts of atmospheric circulation on climate change.

This article presents a wavelet coherence investigation of electroencephalograph (EEG) readings acquired from patients with Alzheimer disease (AD)  and healthy controls. Pairwise electrode wavelet coherence is calculated over each frequency band (delta, theta, alpha, and beta). For comparing the synchronization fraction of 2 EEG signals, a wavelet coherence fraction is proposed which is defined as the fraction of the signal time during which the wavelet coherence value is above a certain threshold. A one-way analysis of variance test shows a set of statistically significant differences in wavelet coherence between AD and controls. The wavelet coherence method is effective for studying cortical connectivity at a high temporal resolution. Compared with other conventional AD coherence studies, this study takes into account the time–frequency changes in coherence of EEG signals and thus provides more correlational details. A set of statistically significant differences was found in the wavelet coherence among AD and controls. In particular, temporocentral regions show a significant decrease in wavelet coherence in AD in the delta band, and the parietal and central regions show significant declines in cortical connectivity with most of their neighbors in the theta and alpha bands. This research shows that wavelet coherence can be used as a powerful tool to differentiate between healthy elderly individuals and probable AD patients.

Vegetation dynamics are sensitive to climatic warming and are affected by individual or combined climatic factors at different temporal scales with different intensities. Previous studies have unraveled the relationships between vegetation dynamics and individual climatic factors; however, it is unclear whether the effects of single or combined climatic factors on vegetation dynamics are dominant for different temporal scales, vegetation types, and climatic regions. The objective of this study was to explore scale-specific univariate and multivariate controls on vegetation over the period 1982–2015 using bivariate wavelet coherence (BWC), multivariate wavelet coherence (MWC), and multidimensional empirical mode decomposition (MEMD). The results indicated that significant vegetation dynamics were located mainly at scales of 1, 0.5, and 0.3 years. Vegetation variations were divided into seasonal (≤ 1 year), short-term (1–4 years), medium-term (4–8 years), and long-term (> 8 years) scales. The combined explanatory powers of seven climatic factors on the vegetation were greater at the short-term and long-term scales, whereas individual climatic factors, such as precipitation or temperature, might affect vegetation dynamics in some climatic regions at the seasonal and medium-term scales. The combined effect of climatic factors in the grassland of the Tibetan Plateau (TP) and the temperate grassland of Inner Mongolia (TGIM) were the greatest, which were 65.06% and 59.53%, respectively. The explanatory powers of climate on crop dynamics in both temperate humid and subhumid Northeast China and the TP were around 47%, whereas the controls of climate on crops in both the TGIM and the temperate and warm-temperate desert of Northwest China were around 39%. Cropland farming practices could alleviate the spatial variation of the relationships between climate and vegetation while enhancing the temporal difference of their relationships. Additionally, the dominant influencing factor among different regions varied greatly at the medium-term scale. Collectively, the results might provide an alternative perspective for understanding vegetation evolution in response to climatic changes in China.