Suchergebnisse

In persons with multiple sclerosis (PwMS), synchronizing walking to auditory stimuli such as to music and metronomes have been shown to be feasible, and positive clinical effects have been reported on step frequency and perception of fatigue. Yet, the dynamic interaction during the process of synchronization, such as the coupling of the steps to the beat intervals in music and metronomes, and at different tempi remain unknown. Understanding these interactions are clinically relevant, as it reflects the pattern of step intervals over time, known as gait dynamics. 28 PwMS and 29 healthy controls were instructed to walk to music and metronomes at 6 tempi (0-10% in increments of 2%). Detrended fluctuation analysis was applied to calculate the fractal statistical properties of the gait time-series to quantify gait dynamics by the outcome measure alpha. The results showed no group differences, but significantly higher alpha when walking to music compared to metronomes, and when walking to both stimuli at tempi+8,+10% compared to lower tempi. These observations suggest that the precision and adaptation gain differ during the coupling of the steps to beats in music compared to metronomes (continuous compared to discrete auditory structures) and at different tempi (different inter-beat-intervals). ; We acknowledge the Methusalem project (awarded by the Flemish Government) at UGent and the UHasselt BOF grant (BOF16DOC41) for funding this study. ; Moumdjian, L (corresponding author), Univ Ghent, IPEM Inst Psychoacoust & Elect Mus, Fac Arts & Philosophy, Ghent, Belgium ; Hasselt Univ, Fac Rehabil Sci, REVAL Rehabil Res Ctr, Hasselt, Belgium. lousin.moumddjian@ugent.be

Abstract. Indonesia is one of the most seismically active regions in the world and mitigation of seismic hazard is important. It is reported that Ultra low frequency (ULF) geomagnetic anomalies are one of the most convincing phenomena preceding large earthquakes (EQs). In this paper we have analyzed geomagnetic data at Pelabuhan Ratu (PLR) (7.01° S, 106.56° E), Sukabumi, West Java, Indonesia, from 1 September 2008 to 31 October 2010. There are twelve moderate–large EQs (M ≥ 5) within 160 km from the station during the analyzed period. The largest one is the M =7.5 EQ (depth = 57 km, epicentral distance = 135 km, 2 September 2009) based on EQ catalog of Indonesian Meteorological, Climatological and Geophysical Agency (BMKG). To investigate the ULF geomagnetic anomalous variations preceding all the EQs, spectral density ratio at the frequency range of 0.01 ± 0.003 Hz based on wavelet transform (WT) and detrended fluctuation analysis (DFA) have been carried out. The spectral density ratio results show the enhancements a few weeks before the largest EQ. The enhancement persists about one week and reaches a maximum on 16 August 2009. At the same time, the result of the DFA presents the decrease of α value. For other EQs, there are no clear increases of the spectral density ratio with simultaneous decrease of α value. When these phenomena occur, the value of Dst index shows that there are no peculiar global geomagnetic activities at the low latitude region. The above results are suggestive of the relation between the detected anomalies and the largest EQ.

Abstract. The time dynamics of seismicity of Aswan area (Egypt) from 2004 to 2010 was investigated by means of the (i) Allan Factor, which is a powerful tool allowing the capture of time-clusterized properties of temporal point processes; and the (ii) detrended fluctuation analysis, which is capable of detecting scaling in nonstationary time series. The analysis was performed varying the depth and the magnitude thresholds. The 2004–2010 Aswan seismicity is characterized by significant three-fold time-clustering behaviors with scaling exponents ~0.77 for timescales between 104.16 s and 105.14 s, ~0.34 for timescales between 105.14 s and 106.53 s, and ~1 for higher timescales. The seismic interevent times and distances are characterized by persistent temporal fluctuations for most of the magnitude and depth thresholds.

Abstract. We examine the recent report of Febriani et al. (2014) in which the authors show changes in ULF magnetic field data prior to the M7.5 Tasikmalaya earthquake that occurred south of Java, Indonesia, on 2 September 2009. Febriani et al. (2014) state that the magnetic changes they found may be related to the impending earthquake. We do not agree that the pre-earthquake magnetic changes shown in Febriani et al. (2014) are seismogenic. These magnetic changes, indeed, are too closely related to global geomagnetic disturbances to be regarded as being of seismic origin.

This paper mainly studies the market nonlinearity and the prediction model based on the intrinsic generation mechanism (chaos) of Bitcoin's daily return's volatility from June 27, 2013 to November 7, 2019 with an econophysics perspective, so as to avoid the forecasting model misspecification. Firstly, this paper studies the multifractal and chaotic nonlinear characteristics of Bitcoin volatility by using multifractal detrended fluctuation analysis (MFDFA) and largest Lyapunov exponent (LLE) methods. Then, from the perspective of nonlinearity, the measured values of multifractal and chaos show that the volatility of Bitcoin has short-term predictability. The study of chaos and multifractal dynamics in nonlinear systems is very important in terms of their predictability. The chaos signals may have short-term predictability, while multifractals and self-similarity can increase the likelihood of accurately predicting future sequences of these signals. Finally, we constructed a number of chaotic artificial neural network models to forecast the Bitcoin return's volatility avoiding the model misspecification. The results show that chaotic artificial neural network models have good prediction effect by comparing these models with the existing Artificial Neural Network (ANN) models. This is because the chaotic artificial neural network models can extract hidden patterns and accurately model time series from potential signals, while the benchmark ANN models are based on Gaussian kernel local approximation of non-stationary signals, so they cannot approach the global model with chaotic characteristics. At the same time, the multifractal parameters are further mined to obtain more market information to guide financial practice. These above findings matter for investors (especially for investors in quantitative trading) as well as effective supervision of financial institutions by government.

Abstract. Scaling behaviour in nonstationary time series can be successfully detected using the detrended fluctuation analysis (DFA). Observational time series often do not show a stable and uniform scaling behaviour, given by the presence of a unique clear scaling region. The deviations from uniform power-law scaling, which suggest the presence of changing dynamics in the system under study, can be identified and quantified using an appropriate instability index. In this framework, the scaling behaviour of the 1981–2007 seismicity in Umbria-Marche (central Italy), which is one of the most seismically active areas in Italy, was investigated. Significant deviations from uniform power-law scaling in the seismic temporal fluctuations were revealed mostly linked with the occurrence of rather large earthquakes or seismic clusters.