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In this study, a TEC variation window value was selected based on the wavelet power spectrum method to analyze the seismic–ionospheric coupling relationship. In the full-time domain, a 27-day periodicity of the wavelet power spectrum was obtained that passed the 95% significance test. The sliding interquartile range method was used to analyze earthquakes above Mw6.5 in Japan from 2011 to 2022, excluding the hybrid effects between earthquakes close to one another. The sunspot number (SSN), 10.7 cm radio flux (F10.7), total solar irradiance (TSI), solar wind velocity (Vsw), geomagnetic activity index in the equatorial region (DST), and global geomagnetic activity index (KP) were used as indices representing solar and geomagnetic activity. After removing solar and geomagnetic interference from ionospheric anomaly changes using the sliding interquartile range method, the TEC anomaly changes before the earthquake were verified as being caused by the earthquake and analyzed. The statistical analysis of ionospheric total electron content (TEC) anomalies showed that earthquake magnitude was positively correlated with the amplitude of TEC anomalies but not linearly. The occurrence time of ionospheric anomalies lagged behind to some extent with the increase in earthquake magnitude. Additionally, abnormal changes on the 29th day (15 February 2022) before the 20th earthquake did not conform to previous research rules. According to the lithosphere–atmosphere–ionospheric coupling (LAIC) mechanism and global ionospheric map (GIM) studies, the TEC anomaly was consistent with the vertical projection of the epicenter with obvious regularity. The results show that these TEC anomalies may be related to earthquakes. [ABSTRACT FROM AUTHOR]

In this study, a TEC variation window value was selected based on the wavelet power spectrum method to analyze the seismic–ionospheric coupling relationship. In the full-time domain, a 27-day periodicity of the wavelet power spectrum was obtained that passed the 95% significance test. The sliding interquartile range method was used to analyze earthquakes above Mw6.5 in Japan from 2011 to 2022, excluding the hybrid effects between earthquakes close to one another. The sunspot number (SSN), 10.7 cm radio flux (F10.7), total solar irradiance (TSI), solar wind velocity (Vsw), geomagnetic activity index in the equatorial region (DST), and global geomagnetic activity index (KP) were used as indices representing solar and geomagnetic activity. After removing solar and geomagnetic interference from ionospheric anomaly changes using the sliding interquartile range method, the TEC anomaly changes before the earthquake were verified as being caused by the earthquake and analyzed. The statistical analysis of ionospheric total electron content (TEC) anomalies showed that earthquake magnitude was positively correlated with the amplitude of TEC anomalies but not linearly. The occurrence time of ionospheric anomalies lagged behind to some extent with the increase in earthquake magnitude. Additionally, abnormal changes on the 29th day (15 February 2022) before the 20th earthquake did not conform to previous research rules. According to the lithosphere–atmosphere–ionospheric coupling (LAIC) mechanism and global ionospheric map (GIM) studies, the TEC anomaly was consistent with the vertical projection of the epicenter with obvious regularity. The results show that these TEC anomalies may be related to earthquakes.

Key points 1. To explain the data of the 2021 earthquake swarm in Hualien 2. To describe the Morlet wavelet and fluctuation methods 3. Results show that the memory effect is stronger for the time sequence of magnitudes than for that of inter-event times and higher for M L ≥ 3 events than for M L ≥ 4 ones; and the short- term corrected memory effect was operative in the Hualien swarms

The gravest challenge for economic sustainability is the undetermined growth in the financial and economic risks of the nation, which need to be overcome with adequate measures without compromising economic growth. The uncertainty of economic factors produces fluctuations in the financial sector and makes them more vulnerable. However, the existing literature has not significantly focused on the economic and financial risk challenge for sustainable economic growth. Therefore, to fill the gap, an in-depth study is imperative to explore the association between these risks. To do so, this study incorporates both economic and financial risk to determine how risks are interconnected across time (frequency) and how they are linked by utilizing quarterly data from 1984-Q1 to 2020-Q4 and by applying both the “wavelet power spectrum (WPS)” and “wavelet coherence (WTC)” approaches, to examine the time-frequency dependency of each variable on the other. The findings of WTC revealed that the economic and financial risks have a positive dependency on each other in India at high, medium, and low frequencies. Likewise, the wavelet power spectrum outcomes reflect the high economic and financial risks vulnerability during 1991, 1992, and 1996. In addition, for the robustness check, the study employed both the “quantile regression (QR)” and “quantile-on-quantile regression (QQR)”. Both the QQR and QR endorsed the positive association between FR and ER. Hence, our paper is the first research of its kind for the Indian economy, and it extends to the existing literature by examining the link between the two most significant indicators in terms of both time and frequency dependency. The findings in our paper offer excellent perspectives for investors and policymakers to assess prospects for investment and policy changes if necessary.

The Bank of Zambia has one of the highest central bank turnover averages in Africa and the inflationary effects of the low central bank autonomy are yet to be empirically quantified. This study develops an index of central bank turnover for the Zambian economy extending from 1984 to 2020 and applies continuous wavelet coherence techniques to examine its co-movement with inflation in a time-frequency domain. We find negative co-movements between the variables across the entire time window with higher (lower) frequency correlations being dominant in the pre-2000 (post-2000) period. Moreover, the phase difference dynamics indicate that central bank turnover has led inflation in all periods except during the 1990–1995 and 2016–2020 periods where reverse causality is observed. Altogether, our study paints a detailed narrative on the relationship between the variables for the Zambian economy and offers policy advice aimed at strengthening the independence and credibility of the country’s central bank.

While we can say that there is a global market for crude oil, we cannot say the same for natural gas. There is a strand of literature that argues that, in the last decades, gas markets have become less regional and more global. We use wavelets to test this hypothesis and conclude otherwise: although the European and Japanese gas markets are significantly synchronized, they are much less than the oil markets, which we take as the benchmark. We also show that the North American gas market fluctuations are independent of the other gas markets. Finally, we show that the existing synchronization between gas markets almost vanishes once one filters out the effect of oil price variations, suggesting that it is the global oil market that connects the regional gas markets. [ABSTRACT FROM AUTHOR]

Wavelet power spectrum (WPS) techniques were used to evaluate the temporal structure of the clearness index and relative sunshine duration, using monthly values of clearness index and relative sunshine duration between 1995 and 2010. The significant years are featured by high wavelet coefficients of both the clearness index and relative sunshine duration. Low wavelet coefficients noticed during the selected years might emerge from the increase in turbidity and cloudiness, indicating that cloudiness has the greater impact on the solar radiation resulting in weak sky condition. Bootstrap method was also used to acquire regression correlations between clearness index and relative sunshine duration. The correlation coefficient between the monthly values of clearness index and relative sunshine duration for ABK (0.32), ENU (0.31), JOS (0.40), MAID (0.46), OWE (0.25), PH (0.46), SOK (0.65), YOL (0.58). The climatic condition and weather patterns of locations have an impact on the amount solar energy received.

The wavelet power spectrum and nonlinear dynamics time series techniques were used to examine the ionospheric electric current disturbance (Diono) during the geomagnetic storms of 9–11 November 2004 and 14–16 May 2005. These storms were characterized by high solar wind speed from coronal mass ejections (CMEs). The time series of the solar wind parameters (interplanetary magnetic field, IMF Bz and solar wind speed, Vx) and geomagnetic indices (Symmetric disturbance field, SYM-H, eastward electrojet, AU and westward electrojet AL), were used to monitor solar wind conditions and the geomagnetic perturbation, respectively. The horizontal component of the geomagnetic field obtained from six ground-based magnetometer stations was also analyzed. The high latitudes stations (ESK, NUR, LER and SOD) show higher energy wavelet coefficient of Diono with high frequencies, due to the existence of ionospheric currents at high latitudes during geomagnetic storm. The mid and low latitude stations (HER and AAE) display low energy wavelet coefficient with low frequency. The low latitude geomagnetic stations display high values of Lyapunov exponent (LE) during 9–11 November 2004 and 14–16 May 2005 of the geomagnetic storm. The low values of Lyapunov exponent might be as result of the internal dynamics restructuring during geomagnetic storm. The electrodynamics of the magnetosphere-ionosphere due to solar wind influenced the chaotic signature of Diono. The solar wind is a stochastic driver which contributes in the generation of geomagnetic disturbance in the Earth's magnetosphere due to CMEs that generates the geomagnetic storms. [ABSTRACT FROM AUTHOR]

An earthquake sequence that occurred in the Hualien area during April 7 to August 30, 2021 has been recognized as two swarms by the Central Weather Bureau. Its earthquakes with ML ≤ 6.2 (ML = local magnitude) and focal depths ≤ 25 km were located in an area from 23°46’ N to 24°04’ N and from 121°25’ E to 121°42’ E. The Morlet wavelet technique is applied to analyze the dominant periods of temporal variations in numbers of daily events for the earthquake sequence in two magnitude ranges, i.e., ML ≥ 3 and ML ≥ 4. Results show that the dominant periods are 30.8 and 38.0 days when ML ≥ 3; while the dominant period does not exist when ML ≥ 4. The fluctuation analysis technique in the natural time domain is used to study the memory effect in the swarm for two magnitude ranges, i.e., ML ≥ 3 and ML ≥ 4. Calculated results show that the memory effect is stronger for the time sequence of magnitudes than for that of inter-event times and higher for ML ≥ 3 earthquakes than for ML ≥ 4 events. Consequently, only the short-term corrected memory effect was operative in the earthquake sequence of the Hualien swarms.Key points: To explain the data of the 2021 earthquake swarm in Hualien To describe the Morlet wavelet and fluctuation methods Results show that the memory effect is stronger for the time sequence of magnitudes than for that of inter-event times and higher for ML ≥ 3 events than for ML ≥ 4 ones; and the short- term corrected memory effect was operative in the Hualien swarms [ABSTRACT FROM AUTHOR]

Introduction : Hemorrhagic fever with renal syndrome (HFRS) is a life-threatening public health problem in China, accounting for ~90% of HFRS cases reported globally. Accurate analysis and prediction of the HFRS epidemic could help to establish effective preventive measures.Materials and Methods : In this study, the geographical information system (GIS) explored the spatiotemporal features of HFRS, the wavelet power spectrum (WPS) unfolded the cyclical fluctuation of HFRS, and the wavelet neural network (WNN) model predicted the trends of HFRS outbreaks in mainland China.Results : A total of 209,209 HFRS cases were reported in mainland China from 2004 to 2019, with the annual incidence ranged from 0 to 13.05 per 100,0000 persons at the province level. The WPS proved that the periodicity of HFRS could be half a year, 1 year, and roughly 7-year at different time intervals. The WNN structure of 12-6-1 was set up as the fittest forecasting model for the HFRS epidemic.Conclusions : This study provided several potential support tools for the control and risk-management of HFRS in China.

Periodicity correlations between roadside PM $_{2.5}$ concentration and traffic volume are difficult to explore due to the complicated process of production and diffusion of PM $_{2.5}$ : a reaction of vehicle emission with atmosphere, dependence with wind speed, pavement width, vehicle speed, vehicle type, etc. We propose a framework to explore the periodicity correlation between roadside PM $_{2.5}$ concentration and traffic volume using wavelet transform. The framework utilizes wavelet spectrum to obtain the characteristic periods of PM $_{2.5}$ concentration and traffic volume, and utilizes cross-wavelet spectrum to explore the hysteresis response of PM $_{2.5}$ concentration with respect to traffic volume. Different from traditional research work which handle pollutants and traffic data of macro scales, our study examines the micro-scale relationship of PM $_{2.5}$ concentration and traffic volume. We conduct experiments from four urban streets of Beijing, and reveal two micro-scale rules: (a) the characteristic period of PM $_{2.5}$ concentration approximates the characteristic period of traffic volume, and both of them are significantly affected by the traffic signal cycle; (b) PM $_{2.5}$ concentration exhibits a time delay within 0.3–0.9 minutes with respect to traffic volume. The research will benefit the further study on the evolution of PM $_{2.5}$ concentration and traffic volume, and provide a reference for the mitigation of PM $_{2.5}$ pollution by traffic control as well as practical traffic pollutant regulations in metropolises. [ABSTRACT FROM AUTHOR]

The solar quiet (Sq) variations of horizontal and vertical (SqH and SqZ) components of the geomagnetic field obtained from both the Northern Hemisphere and Southern Hemisphere of the International Real-Time Magnetic Observatory Network (INTERMAGNET) during solar maximum year 2001 were investigated. The results show enlargement of the SqH component of the geomagnetic field during the daytime, attributed to equatorial electrojet (EEJ) current closer to the geomagnetic equator at the electrojet stations (BNG and MBO), which are produced from large eastward flow of the current. It was observed that SqZ is positive at the southward and negative at the northward hemispheres. SqZ is amplified at HER and HBK around the daytime. Wavelet power spectrum based approach was employed to analyse the SqH, SqZ, and rate of induction (SqZ/SqH) time series in a sequence of time scaling from January to December. The higher energy of SqH and SqZ of the wavelet coefficients is noticeable at high frequency. The monthly variation rate of induction (SqZ/SqH) analyses during the Sq variations are associated with the influence of equatorwards penetration of electric fields from the field-aligned current, Earth conductivity, effect of the ocean, and ionospheric conductivity. [ABSTRACT FROM AUTHOR]

By means of the Continuous Morlet Wavelet Transform fostering covariance/correlation, lead-lag causal relationship as well as coherency via the wavelets analysis, we explore the two indices on the Ghana Stock Exchange, the larger Ghana Stock Exchange Composite Index (GSE-CI) and the smaller Ghana Stock Exchange Financial Services Index (GSE-FSI) with the US dollar and Euro. Using daily data from January 2011 to December 2016 we confirm that there is mixed interplay of lead-lag relationships, mostly strong at lower frequencies, among the indices and the two most important exchange rates in Ghana. This paper serves as the first of its kind in the literature owing to its rich methodology and variables employed. Our study implies that investing selectively in either GSE-CI or GSE-FSI is very important and differences in co-movement of GSE-CI and GSE-FSI with the exchange rates. We reveal that there is narrowly identifiable lead-lag relationship between GSE-CI and USD/GHS and GSE-FSI and USD/GHS. Investors as revenue maximisation agents should consider the time and frequency spaces of the GSE-CI and GSE-FSI in their investment decisions involving diversification with the USD and EUR both in the short- and medium-terms (up to four years). Further, any policy meant to influence performance on the Ghana Stock Exchange should consider the time and frequency domains of the equities traded on the exchange.

This paper investigates equity market risk and co-movements between the Lithuanian stock market and the Central European stock markets. We cover the equity market returns both in time and frequency domains. We focus our studies on the changes of the market risk and co-movements of the Lithuanian and the Central European markets returns during the period of 2000-2018. The wavelet analysis was applied to segregate the returns across different time horizons (frequencies). Our findings corroborate the findings from other authors, namely that crisis periods have a great impact on the interrelations of the Central European and Lithuanian markets. We discover that volatility is concentrated in the medium and long periods (medium and low frequencies) from 1 to 3,5 years for all the markets under consideration. The absolute maximum of volatility is achieved at the period of the frequencies corresponding to the period of 3 years. We found that the co-movements with Poland, the Czech Republic and Hungary are slightly lower after the announcement of the introduction of the euro in Lithuania by the European Commission. From the investment diversification point of view, the investment horizon plays a crucial role for the level of co-movements. Our conclusion is that for Lithuanian investors, diversification with Central European markets is not useful for long horizons, because of the high co-movements. The benefit of the diversification can be achieved for the investors with time horizons less than 1 year. [ABSTRACT FROM AUTHOR]

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In three different ways of lead–lag causal relationship, covariance/correlation and coherence, we apply the wavelets analysis via the Continuous Morlet Wavelet Transform to delineate the significant frequency–time domain lead–lag relationships for the West African Monetary Zone member countries for real US dollar exchange rates and their absolute log returns from January 2001 to April 2015. The results indicate that lead–lag associations at different periodicities vary across the countries. No one country comes off as leading conveniently for both real and absolute returns of the exchange rates. Our results corroborate other evidences of non-convergence of exchange rates in the monetary zone, which hinders the eventual implementation of the single currency in the ECOWAS region.

This paper aims to assess the low-frequency dynamics of energy inflation's volatility across a sample of six OECD advanced countries. The traditional argument attributes a transitory effect to energy inflation putting less attention on the longer-term economic consequences. Using the wavelet power spectrum, we provide evidence that most of energy inflation volatility is mainly concentrated at the frequency bands between approximately 1.5 to 5.5 years. Potentially, this result can have nontrivial consequences on how monetary policy should face changes in energy prices. • Energy inflation's volatility is not concentrated in the short-run. • According to power spectra, it operates at frequencies between 1.5 and 5.5 years. • Monetary policy cannot dismiss energy inflation as a transitory phenomenon. [ABSTRACT FROM AUTHOR]

The gravest challenge for economic sustainability is the undetermined growth in the financial and economic risks of the nation, which need to be overcome with adequate measures without compromising economic growth. The uncertainty of economic factors produces fluctuations in the financial sector and makes them more vulnerable. However, the existing literature has not significantly focused on the economic and financial risk challenge for sustainable economic growth. Therefore, to fill the gap, an in-depth study is imperative to explore the association between these risks. To do so, this study incorporates both economic and financial risk to determine how risks are interconnected across time (frequency) and how they are linked by utilizing quarterly data from 1984-Q1 to 2020-Q4 and by applying both the "wavelet power spectrum (WPS)" and "wavelet coherence (WTC)" approaches, to examine the time-frequency dependency of each variable on the other. The findings of WTC revealed that the economic and financial risks have a positive dependency on each other in India at high, medium, and low frequencies. Likewise, the wavelet power spectrum outcomes reflect the high economic and financial risks vulnerability during 1991, 1992, and 1996. In addition, for the robustness check, the study employed both the "quantile regression (QR)" and "quantile-on-quantile regression (QQR)". Both the QQR and QR endorsed the positive association between FR and ER. Hence, our paper is the first research of its kind for the Indian economy, and it extends to the existing literature by examining the link between the two most significant indicators in terms of both time and frequency dependency. The findings in our paper offer excellent perspectives for investors and policymakers to assess prospects for investment and policy changes if necessary., (© 2023 The Authors. Published by Elsevier Ltd.)

ML ≥ 3 earthquakes (ML = local magnitude) that occurred in the Taipei Metropolitan Area (TMA) from 1973 - 2013 are selected to study the dominant seismicity period of this area. The epicentral distribution and temporal sequences of earthquake magnitudes are simply described. These earthquakes can be divided into two groups: one for events shallower than 40 km and one for events deeper than 60 km. Shallow earthquakes are located mainly in the 0 - 10 km depth range north of 25.1°N, and down to 35 km for those south of 25.1 °N. Deep events are located in the subduction zone, with a dip angle of about 70°. The Morlet wavelet technique is applied to analyze the dominant periods of temporal variations in numbers of monthly earthquakes in the shallow and deep ranges for three magnitude ranges, i.e., ML ≥ 3, 4, and 5. The results show that for shallow earthquakes the dominant periods are 15.4, 30.8,66.1, and 132.2 months when ML ≥ 3 and 30.8 months when ML ≥ 4; while for deep earthquakes, the dominant periods are 16.5 and 141.7 months when ML ≥ 3 and 141.7 months when ML ≥ 4. The dominant period cannot be obtained for both shallow and deep ML ≥ 5 earthquakes. [ABSTRACT FROM AUTHOR]

The hidden Markov model (HMM) has been widely used in regime classification and turning point detection for econometric series after the decisive paper by Hamilton (Econometrica 57(2):357-384, ). The present paper will show that when using HMM to detect the turning point in cyclical series, the accuracy of the detection will be influenced when the data are exposed to high volatilities or combine multiple types of cycles that have different frequency bands. Moreover, outliers will be frequently misidentified as turning points. The present paper shows that these issues can be resolved by wavelet multi-resolution analysis based methods. By providing both frequency and time resolutions, the wavelet power spectrum can identify the process dynamics at various resolution levels. We apply a Monte Carlo experiment to show that the detection accuracy of HMMs is highly improved when combined with the wavelet approach. Further simulations demonstrate the excellent accuracy of this improved HMM method relative to another two change point detection algorithms. Two empirical examples illustrate how the wavelet method can be applied to improve turning point detection in practice. [ABSTRACT FROM AUTHOR]

In this paper, a cross-shore profile evolution model, Uniform Beach Sediment Transport-Time-Averaged Cross-Shore (UNIBEST-TC), is used. The model was developed at WL/Delft hydraulic laboratory in the Netherlands. The model is used to predict wave height in a barred beach (Egmond site, The Netherlands) and the results show that there is a good agreement between the measured and predicted values by the model. In the present study, Morlet wavelet is used to distinguish the breaking waves; it is integrated over frequency to provide the temporal variation of localized total energy. The study shows that the local peaks of the energy densities correspond to the events of wave breaking in the predicted-wave time series. Furthermore, the wave energy distribution shows a tendency to decrease in the off-shore direction of the inner bar. [ABSTRACT FROM AUTHOR]

Abstract: The study of the behavior of ion-channels can provide significant information to detect metal ions and small organic molecules in solution. Discrimination of different ion-channels can be performed by extracting appropriate features from their signals and using them for classification. In this paper, we consider features extracted from the Fourier, wavelet and Walsh power spectra of the ion-channel signals. We compare the performance of all the three sets of features using support vector machines. We perform classification of signals from simulated and real ion-channels and present the results. Results obtained show that the transform domain features achieve high classification rates in addition to high sensitivity and specificity rates. [Copyright &y& Elsevier]

Microarray technologies facilitate the generation of vast amount of bio-signal or genomic signal data. The major challenge in processing these signals is the extraction of the global characteristics of the data due to their huge dimension and the complex relationship among various genes. Statistical methods are used in broad spectrum in this domain. But, various limitations like extensive preprocessing, noise sensitiveness, requirement of critical input parameters and prior knowledge about the microarray dataset emphasise the need for better exploratory techniques. Transform oriented signal processing techniques are successful in many data processing techniques like image and video processing. But, the use of wavelets in analyzing the microarray bio-signals is not sufficiently probed. The aim of this paper is to propose a wavelet power spectrum based technique for dimensionality reduction through gene selection and classification problem of gene microarray data. The proposed method was administered on such datasets and the results are encouraging. The present method is robust to noise since no preprocessing has been applied. Also, it does not require any critical input parameters or any prior knowledge about the data which is required in many existing methods. [ABSTRACT FROM AUTHOR]

Wavelet analysis is a powerful tool that is already in use throughout science and engineering. The versatility and attractiveness of the wavelet approach lie in its decomposition properties, principally its time-scale localization. It is especially relevant to the analysis of non-stationary systems, i.e., systems with short-lived transient components, like those observed in ecological systems. Here, we review the basic properties of the wavelet approach for time-series analysis from an ecological perspective. Wavelet decomposition offers several advantages that are discussed in this paper and illustrated by appropriate synthetic and ecological examples. Wavelet analysis is notably free from the assumption of stationarity that makes most methods unsuitable for many ecological time series. Wavelet analysis also permits analysis of the relationships between two signals, and it is especially appropriate for following gradual change in forcing by exogenous variables. [ABSTRACT FROM AUTHOR]

This study applied a new approach to distinguish between breaking waves and nonbreaking waves in the wind—wave time series through the use of wavelet analysis. In this paper, the wavelet power spectrum is computed to identify the variations in the energy content of the wind—wave time series with time, and then it is integrated over frequency to provide the temporal variation of localized total energy. The study shows that the fluctuations of the time series of wind—wave are highly intermittent, i.e., the energy at different scales varies considerably with time. Furthermore, the local peaks of the energy densities correspond to the events of wave breaking in the wind—wave time series. [ABSTRACT FROM AUTHOR]

Data mining techniques are widely used in many fields. One of the applications of data mining in the field of the Bioinformatics is classification of tissue samples. In the present work, a wavelet power spectrum based approach has been presented for feature selection and successful classification of the multi class dataset. The proposed method was applied on SRBCT and the breast cancer datasets which are multi class cancer datasets. The selected features are almost those selected in previous works. The method was able to produce almost 100% accurate classification results. The method is very simple and robust to noise. No extensive preprocessing is required. The classification was performed with comparatively very lesser number of features than those used in the original works. No information is lost due to the initial pruning of the data usually performed using a threshold in other methods. The method utilizes the inherent nature of the data in performing various tasks. So, the method can be used for a wide range of data. [ABSTRACT FROM AUTHOR]

A mistral event is considered to be the most dangerous of all Mediterranean winds. Its effects can generate high waves, which consequently can affect shipping. In this study the continuous wavelet is used to detect the dispersion and groupings characteristics of waves during a mistral event. During the process of wind-wave interaction, waves are dispersed into groups with respect to their periods and wave growth is taking place within the wave group. The wavelet power spectrum of the time series of a wind wave shows that the energy at different scales varies considerably with time. Furthermore, the narrowing of the frequencies in the phase of wavelet transforms suggests the dispersion of waves. [ABSTRACT FROM AUTHOR]