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Radioactive radon gas poses significant threats to human health. Understanding the complexities of radon distribution and the dynamic relationship with atmospheric parameters will help in mitigating its impact. In this study, Multifractal Detrended Fluctuation Analysis (MF-DFA) and chaos analysis were used to examine the fractal structure in radon gas at La Cueva del Viento, Tenerife, between January 2021 and December 2022. The results showed that radon has multifractal properties in 2021, 2022, and 2021–2022, with values of the spectrum with of about 0.43, 0.49 and 0.44 respectively. The multifractality in radon gas was found to be driven by both long-range correlations and fat-tail distribution. Radon gas concentration at La Cueva del Viento was found to be chaotic in nature, hence, long-term prediction is impossible. Meteorological parameters such as relative humidity, air temperature and pressure were found to contribute to the variation in radon gas concentration within the cave. Relative humidity was observed to have the strongest cross-correlation with radon gas in 2021, 2022, and 2021–2022. The results from this study will help in dosimetric control for both workers and visitors to the cave. • Multifractal and chaos analysis helps us to understand the radon gas fluctuations in a touristic cave. • Rn gas concentration and atmospheric parameters exhibit multifractal characteristics. • Multifractal behaviour is driven by both long-range correlations and fat-tail distribution. • Rn gas and the meteorological parameters were found to have a positive Lyapunov exponent. • This study can help in dosimetric control for both workers and visitors to the cave. [ABSTRACT FROM AUTHOR]

Mono- and multi-fractal geometry have been used to explore the surface characteristics of scanning electron microscopy (SEM) micrographs of the SnS films with thicknesses of 100 nm (SnS1) to 600 nm (SnS4), respectively. For this investigation, the SnS thin films have been grown on fluorine-doped tin oxide (FTO)-coated glass substrate through the thermal evaporation route, and surface morphologies are captured by SEM. Two-dimensional multi-fractal detrended fluctuation analysis (MFDFA) based on the partition function is used to examine whether the surfaces have a multi-fractal nature or not. The partition function is applied to extract the generalized Hurst exponent from the segment size. It has been found that surfaces with higher surface roughness induce substantial nonlinearity and a wider width of the multi-fractal spectrum. The multi-fractal spectrum acquired from the analysis of the geometry and shape of the singularity spectrum is used to quantify the irregularity and complexity of surfaces. Minkowski functionals (MFs) parameters such as volume, boundary, and connectivity were measured for each thin film. Moreover, we tried to correlate the electrical conductivity with the mono- and multi-fractal parameters such as fractal dimension (D f), singularity strength function (Δα), singularity spectrum Δf(α), and it is observed that the conductivity of a thin film decreases with decreasing fractal dimension. The minimum (maximum) resistivity (conductivity) was observed for the surface having a larger fractal dimension. The present investigation suggests that such SnS surfaces, having minimal resistivity and maximum conductivity on the roughest surface, indicate enhanced light trapping capacity and can be utilized as active layers for advanced optoelectronics devices. [ABSTRACT FROM AUTHOR]

Background: Postural impairment is one of the most debilitating symptoms in people with Parkinson's disease (PD), which show faster and more variable oscillation during quiet stance than neurologically healthy individuals. Despite the center of pressure parameters can characterize PD's body sway, they are limited to uncover underlying mechanisms of postural stability and instability.Research Question: Do a multiple domain analysis, including postural adaptability and rambling and trembling components, explain underlying postural stability and instability mechanisms in people with PD?Method: Twenty-four individuals (12 people with PD and 12 neurologically healthy peers) performed three 60-s trials of upright quiet standing on a force platform. Traditional and non-linear parameters (Detrended Fluctuation Analysis- DFA and Multiscale Entropy- MSE) and rambling and trembling trajectories were calculated for anterior-posterior (AP) and medial-lateral (ML) directions.Results: PDG's postural control was worse compared to CG, displaying longer displacement, higher velocity, and RMS. Univariate analyses revealed largely longer displacement and RMS only for the AP direction and largely higher velocity for both AP and ML directions. Also, PD individuals showed lower AP complexity, higher AP and ML DFA, and increased AP and ML displacement, velocity, and RMS of rambling and trembling components compared to neurologically healthy individuals.Significance: Based upon these results, people with PD have a lower capacity to adapt posture and impaired both rambling and trembling components compared to neurologically healthy individuals. These findings provide new insights to explain the larger, faster, and more variable sway in people with PD. [ABSTRACT FROM AUTHOR]

Detection of high impedance faults (HIFs) in low and medium voltage distribution networks has always been challenging due to their lower magnitude and random current characteristics. Conventional overcurrent relays are ineffective in detecting HIFs because of the low fault current associated with them. Consequently, existing HIF detection schemes predominantly rely on features extracted from the frequency, time–frequency, or symmetrical component domains. However, these methods are often limited in their effectiveness under specific conditions, such as particular voltage levels, conductor types, or environmental factors, due to the multifaceted nature of HIF currents, which depend upon a variety of factors such as type of materials, voltage level, wetness of the surface, shape of the conductor, and even the weather conditions. On the other hand, due to the intrinsic presence of arcing in the HIF phenomenon, the resultant fault current consistently assumes a random character, and this inherent randomness can be leveraged as a potential feature for fault detection. This paper proposes a new HIF detection method by analyzing the randomness or unpredictability of the fault current signals. The Detrended fluctuation analysis (DFA) is innovatively employed to assess the unpredictability of the lower frequency component of the zero-sequence current by examining its instantaneous amplitude envelope. The accuracy of the proposed approach is verified with extensive simulation data of fault events under diverse operative conditions. The security of the proposed scheme is also verified under several no-fault transient circumstances, such as capacitor switching and load perturbation. The computational efficiency of the method has been verified through the process-in-loop (PIL) simulation using cost-effective hardware. • Novel application of modified detrended fluctuation analysis to the HIF detection. • The proposed protection method uses of randomness in the zero-sequence current. • Randomness of lower frequency current signal is calculated through DFA and DEMD. • A new HIF simulation model is also presented in this work. • Computational efficiency of the method is tested through process-in-loop simulation. [ABSTRACT FROM AUTHOR]

Background: Adaptive treadmills allow real-time changes in walking speed by responding to changes in step length, propulsion, or position on the treadmill. The stride-to-stride variability, or persistence, of stride time during overground, fixed-speed, and adaptive treadmill walking has been studied, but persistence of propulsion during adaptive treadmill walking remains unknown. Because increased propulsion is often a goal of post-stroke rehabilitation, knowledge of the stride-to-stride variability may aid rehabilitation protocol design.Research Question: How do spatiotemporal and propulsive gait variables vary from stride to stride during adaptive treadmill walking, and how do they compare to fixed-speed treadmill walking?Methods: Eighteen young healthy subjects walked on an instrumented split-belt treadmill in the adaptive and fixed-speed modes for 10 minutes at their comfortable speed. Kinetic data was collected from the treadmill. Detrended fluctuation analysis was applied to the time series data. Shapiro-Wilk tests assessed normality and one-way repeated measures ANOVAs compared between adaptive, fixed-speed, and randomly shuffled conditions at a Bonferroni-corrected significance level of 0.0055.Results: Stride time, stride length, step length, and braking impulse were persistent (α > 0.5) in the adaptive and fixed-speed conditions. Adaptive and fixed-speed were different from each other. Stride speed was persistent in the adaptive condition and anti-persistent (α < 0.5) in the fixed-speed condition. Peak propulsive force, peak braking force, and propulsive impulse were persistent in the adaptive condition but not the fixed-speed condition (α ≈ 0.5). Net impulse was non-persistent in the adaptive and fixed-speed conditions. All variables were non-persistent in the shuffled condition.Significance: During adaptive treadmill walking, increases in propulsive force and impulse persist for multiple strides. Persistence was stronger on the adaptive treadmill, where increased propulsion translates into increased walking speed. For post-stroke gait rehabilitation where increasing propulsion and speed are goals, the stronger persistence of adaptive treadmill walking may be beneficial. [ABSTRACT FROM AUTHOR]

Background: Due to the imposed constant belt speed, motorized treadmills are known to affect linear and nonlinear gait variability outcomes. This is particularly true of patients with Parkinson's Disease where the treadmill can act as an external pacemaker. Self-paced treadmills update the belt speed in response to the subject's walking speed and might, therefore, be a useful tool for measurement of gait variability in this patient population. This study aimed to compare gait variability during walking at self-paced and constant treadmill speeds with overground walking in individuals with PD and individuals with unimpaired gait.Methods: Thirteen patients with Parkinson's Disease and thirteen healthy controls walked under three conditions: overground, on a treadmill at a constant speed, and using three self-paced treadmill modes. Gait variability was assessed with coefficient of variation (CV), sample entropy (SampEn), and detrended fluctuation analysis (DFA) of stride time and length. Systematic and random error between the conditions was quantified.Results: For individuals with PD, error in variability measurement was less during self-paced modes compared with constant treadmill speed for stride time but not for stride length. However, there was substantial error for stride time and length variability for all treadmill conditions. For healthy controls the error in measurement associated with treadmill walking was substantially less.Significance: The large systematic and random errors between overground and treadmill walking prohibit meaningful gait variability observations in patients with Parkinson's Disease using self-paced or constant-speed treadmills. [ABSTRACT FROM AUTHOR]

• Connection between complexity and parameter settings of financial models is analyzed. • Nine popular small-scale financial agent-based models are studied. • Herding behavior and various in-built stabilizing factors increase model complexity. • Increasing number of agents has model-specific effects. We examine the complexity of financial returns generated by popular agent-based models through studying multifractal properties of such time series. Specifically, we are interested in the sensitivity of the models to their parameter settings and whether some patterns emerge in the connection between complexity and a specific type of parameter. We find that (i) herding behavior mostly boosts the model complexity as measured by multifractality, (ii) various in-built stabilizing factors increase model complexity, while (iii) the role of the intensity of choice, the number of agents, as well as the chartists' representation have rather model-specific effects. Finally, the core feature driving the model complexity seems to be the implementation of a switching mechanism governing agents' interactions. The heterogeneous set of nine analyzed models thus offers some universal concepts that hold across their range. Our results also indicate that complex dynamics are observed not only for the benchmark parameter settings but also for other combinations of parameter values for most models. This opens new avenues for future research and specifically motivates examining the models in more detail by focusing on other dynamic properties in addition to the herein presented multifractality. [ABSTRACT FROM AUTHOR]

• DFA of MEG data may aid in the identification of the epileptogenic zone. • Aberrant LRTC correlates with resection area and interictal epileptiform discharges. • Relationship between aberrant LRTCs and resection area showed etiological dependence. To examine the usability of long-range temporal correlations (LRTCs) in non-invasive localization of the epileptogenic zone (EZ) in refractory parietal lobe epilepsy (RPLE) patients. We analyzed 10 RPLE patients who had presurgical MEG and underwent epilepsy surgery. We quantified LRTCs with detrended fluctuation analysis (DFA) at four frequency bands for 200 cortical regions estimated using individual source models. We correlated individually the DFA maps to the distance from the resection area and from cortical locations of interictal epileptiform discharges (IEDs). Additionally, three clinical experts inspected the DFA maps to visually assess the most likely EZ locations. The DFA maps correlated with the distance to resection area in patients with type II focal cortical dysplasia (FCD) (p < 0.05), but not in other etiologies. Similarly, the DFA maps correlated with the IED locations only in the FCD II patients. Visual analysis of the DFA maps showed high interobserver agreement and accuracy in FCD patients in assigning the affected hemisphere and lobe. Aberrant LRTCs correlate with the resection areas and IED locations. This methodological pilot study demonstrates the feasibility of approximating cortical LRTCs from MEG that may aid in the EZ localization and provide new non-invasive insight into the presurgical evaluation of epilepsy. [ABSTRACT FROM AUTHOR]

The conventional extended empirical wavelet transform (EEWT) proposed recently is intended to decompose multivariate signals with clear peaks in power spectra without considering the cases where the signals contain high noise levels. Even when dealing with signals with distinct peaks, the EEWT method can still encounter challenges in properly decomposing the signals. However, plant-wide data from industrial control loops, including controllers' outputs, process variables, and manipulated variables, are commonly corrupted by high levels of noise, which can be introduced at various stages of data acquisition, transmission, and processing within the control system. To address these limitations and ensure the applicability of the EEWT to real-world industrial data with diverse and challenging characteristics, this paper presents an improved version called the improved extended empirical wavelet transform (IEEWT). The IEEWT incorporates noise-reduced power spectra and detrended fluctuation analysis techniques to enhance the decomposition. The proposed method demonstrates accurate multivariate data decomposition for both simulated and real data sets, surpassing the limitations associated with the conventional EEWT. • IEEWT tackles the challenge of high noise levels in signals, enabling accurate decomposition. • By incorporating noise-reduced power spectra, IEEWT enhances the reliability of peak detection. • The use of detrended fluctuation analysis in IEEWT improves the identification of relevant peaks. • IEEWT demonstrates accurate multivariate data decomposition for both simulated and real data sets. • The proposed IEEWT overcomes the limitations of conventional EEWT, ensuring reliable results. [ABSTRACT FROM AUTHOR]

Anthropogenic interventions lead to various direct and indirect impacts on natural ecosystems that are often hindered by natural long-term variability, and thus their detection and evaluation remain challenging. Ecological systems are strongly affected by climate variations that typically exhibit long-term correlations capable of imitating or hindering external trends in finite-time observations, thus complicating their detection and correct attribution to either anthropogenic interventions or natural variability. Here we focus on the quantitative assessment of the alterations in the tree-ring width (TRW) of four tree species in response to the changes in the soil water regime following a drainage experiment in a dwarf-shrub type peatland forest. We consider the long-term effects of the intervention, focusing on two characteristic quantities: the durations of clusters when significant discrepancies with relevant controls could be observed in every single consecutive year, and relative trends in the data reflecting long-term, gradual changes in the ecosystem. By extrapolating pre-drainage TRW dynamics and adjusting for recent climate variations using a multivariate model, we simulate surrogate data series that act as additional controls for the post-drainage time period. By comparing the long-term dynamics of the observational TRW data series against both natural and surrogate controls over several decades following the drainage experiment, we evaluate long-term alterations and gradual trends in the tree growth dynamics and reassess the statistical significance of these effects, taking into account long-term correlations in the natural TRW variations. Our results also indicate pronounced alterations in the drought stress response characterized by significant negative trends in the tree growth dynamics following the 2010 heatwave and associated flash drought in the drained area, while no similar effect could be observed in the undrained area, indicating that the increased productivity of the forest ecosystem following the drainage likely comes at the cost of its reduced drought stress resilience. • Long-term correlations in tree-ring and climate records can imitate or hinder trends. • Extrapolated pre-experimental tree-ring data series can be used as surrogate control. • Significance of multi-year discrepancies can be re-evaluated from cluster durations. • Drainage increases productivity of forests at the cost of drought stress resilience. [ABSTRACT FROM AUTHOR]

• Detrended fluctuation analysis was applied to heart failure patients. • Short-term heart rate regulation was impaired in preserved ejection fraction. • Short and long-term heart rate regulation was impaired in heart failure. • This approach may predict autonomic dysfunction in preserved ejection fraction. The impairment of short-term heart rate regulation in patients with heart failure with preserved ejection fraction (HFpEF) can cause acute hemodynamic collapse. Detrended fluctuation analysis (DFA) is a useful tool for the diagnosis of heart diseases and the prediction of mortality. In DFA, the short-term scaling exponent α is decreased in heart failure. However, its change in HFpEF patients remains unclear. Twenty patients diagnosed with HFpEF [defined as brain natriuretic peptide (BNP) >100 pg/mL, ejection fraction (EF) ≥50%, and without significant valvular disease], 20 diagnosed with non-HFpEF (BNP > 100 pg/mL and EF < 50%), and 20 control subjects generally matched for age and gender were enrolled. Holter electrocardiography was performed, and heart rate variability was calculated. In the DFA, the scaling exponents in 1000 beats were calculated for each 15-min segment and the average of all segments was used. We compared both the short-term (<11 beats, α1) and long-term (≥11 beats, α2) scaling exponents among the three groups. In the HFpEF, non-HFpEF, and control groups, α1 was 0.73 ± 0.27, 0.66 ± 0.29, and 1.01 ± 0.20 (p < 0.01), and α2 was 0.95 ± 0.08, 0.88 ± 0.11, and 0.96 ± 0.07 (p < 0.01), respectively. The α1 exponent was significantly decreased in the HFpEF group (p < 0.01 vs. control) and the non-HFpEF group (p < 0.01 vs. control), while the α2 exponent was significantly decreased in the non-HFpEF group only (p < 0.05 vs. HFpEF and control). Short-term heart rate regulation is impaired in patients with HFpEF, while patients with non-HFpEF have both short-term and long-term impairment. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of the changing behaviors of precipitation concentration and intensity plays a pivotal role in water resource management. Hence, we investigated the spatiotemporal changing behaviors of frequency and intensity of 13 precipitation indices and their probable causes of changes in precipitation systems. This study used daily precipitation datasets from 23 sites in Bangladesh and six atmospheric circulation indices during 1975–2017. The results showed that the precipitation concentration index (LCI) varies between 0.57 and 0.63, and the highest value was found in the southeastern region. The precipitation days frequency indices such as AD, LPD, MPD, HPD, and WPD have significantly increased in Bangladesh while precipitation intensity indices such as AII, LPI, MPI, HPI, and WPI have significantly declined; all types of indices have clear rapid changes. The results of detrended fluctuation analysis (DFA) exhibit long-term correlations among all precipitation indices, suggesting that these indices will sustain their present trend line in the upcoming period. The Sunspot (SS) and East Asian Summer Monsoon Index (EASMI) had a negative influence on ACI and South Asian Summer Monsoon index (SASMI) had a strong positive influence on precipitation days frequency indices. The significance analysis using the random forest (RF) algorithm showed that SS is the largest contributing factor affecting the precipitation systems in Bangladesh. ECMWF ERA5 reanalysis datasets revealed that elevating summer geopotential height, higher anticyclonic anomaly, increasing low and decreasing high cloud covers and lower solar radiation with adequate moisture divergence fluxes contributed to variations in precipitation extremes in Bangladesh. Linear trend of Arid days, AD (a), Sequential Mann-kendall test to detect rapid change (b), DFA long term forecasting of ACI (c) and Cross wavelet between SASMI and ACI (d). Unlabelled Image • Concentration index (LCI) vary between 0.57 and 0.63. The highest value was in southeastern part and the lowest LCI value was in eastern part. • Precipitation days frequency indices significantly increased, whereas precipitation intensity indices significantly decreased. • Sunspot is the most important factor influencing the precipitation systems using the RF model. • SS and EASMI had negative (or positive) impacts, while SASMI had positive (or negative) effect on most precipitation indices. [ABSTRACT FROM AUTHOR]

Background: The fractal dynamics of gait variability in people with Parkinson's disease has been studied by applying the detrended fluctuation analysis (DFA) to short time series (<200 strides). However, DFA is sensitive to time series length, and it is unclear if DFA results from short time series are reliable and if they reflect the fractal dynamics of longer time series.Research Question: Is DFA reliable when applied to short time series?Methods: We applied DFA to stride time series from five 3-min trials and one 15-min trial in 12 people with Parkinson's disease, 14 healthy older adults and 14 healthy young adults walking overground. Within each group, intraclass correlations (ICC 3,1) were performed to assess the reliability of i) the five 3-min trials together, ii) each 3-min trials to the 15-min trial, and iii) the first 150 strides from the 15-min trial to the full 15-min trial.Results: Our three main findings are that 1) stride time α-DFA values are not consistent from trial-to-trial for short stride time series, 2) stride time α-DFA values from each 3-min trials are not consistent when compared to stride time α-DFA values from a 15-min trial, and 3) stride time α-DFA values from the first 150 strides of the 15-min trial are not consistent when compared to α-DFA values from the full 15-min trial.Significance: Our results confirm that α-DFA values from 3-min walking trials are not reliable, and that they do not reflect the scale invariant properties of longer time series. This suggests that previous studies assessing the fractal dynamics of gait variability from about 3-min walking must be interpreted with caution. A major clinical implication is that DFA cannot be used to study gait in people unable to perform 500 strides continuously. [ABSTRACT FROM AUTHOR]

• We apply Detrended Fluctuation Analysis to time series of metrics on patent collaborations. • These time series show positive, long-term correlation properties at continental scale. • Different qualitative and quantitative behaviors emerge at EU national level. • We shed light on which countries have stronger relationships with the US according to DFA. • Several associations between DFA exponents and national topological features emerge. Within the perimeter of patent collaboration networks, the average distance of collaborations and the number of countries involved per each collaboration have been shown to have increased steadily in time. Less attention, though, has been devoted to assessing whether this growth of cross-country collaborations is stable in time. To address this scientific question we focus on the identification of long-term correlations (i.e. temporal persistence). Our data set consists of time series of yearly average collaboration radii and of cross-border links in the Euro-American subsystem of the global collaboration network for the period 1978–2014. To investigate the fundamental persistence properties of these time series, we use Detrended Fluctuation Analysis, a method that allows us to measure long-term correlations in detrended signals. Also, we devise a general and original procedure to assess the statistical significance of results for short time series. Our results, showing that long-term correlations do exist in the majority of our signals, reinforce the hypothesis of a diminishing role of geographical distance in technological collaborations. Results at national level show that a significant degree of heterogeneity in persistence parameters can be detected within Europe, irrespectively of the efforts towards the set-up of an integrated European Research Area. [ABSTRACT FROM AUTHOR]

Abstract Following the new advances in encryption and network computing, Bitcoin emerged as a private sector system facilitating peer-to-peer exchange via distributed ledgers based on blockchains, driving a transformational change towards a global economy outside the core financial system. The main purpose of this paper is to examine the multifractal properties of the Bitcoin price using high frequency data. The methods used are the wavelet transform modulus maxima and the multifractal detrended fluctuation analysis. The results indicate that Bitcoin exhibits a large degree of multifractality in all examined time intervals, and the main source of multifractality is attributed to the high kurtosis and the fat distributional tails of the series returns. Highlights • Encryption & network computing enabled a transformational change in markets via Bitcoin. • We explore the fractal dynamics of Bitcoin using high frequency data. • A novel hybrid of wavelet modulus maxima & detrended fluctuation analysis is used. • Fractality is observed in all time intervals. • The main drivers are high kurtosis & fat distributional tails. [ABSTRACT FROM AUTHOR]

Abstract Detrended Fluctuation Analysis has been used in several fields of science to study the statistical properties of trend stationary and nonstationary time-series. Its application to financial data has produced important results concerning long-range correlations and long-memory. However, these results may be contaminated if the researcher attributes to nonstationary trends the effect of stationary trends with endogenous structural breaks. Our paper proposes a modified DFA model where boxes to determine local trends are replaced by endogenous structural break windows. We also allow local trends fitted by quadratic functions and use squared residuals in place of patchy standard deviations to study the magnitude of the power-law exponent. The results show that our modified DFA model performs better than the fixed length alternatives originally proposed, and is, therefore, a suitable model to fit with financial data. Consistently with previous findings, our results show positive long-range correlation in all indices with the higher value for emerging markets. Highlights • Quadratic functions with negative concavity track integrated price returns. • Squared residuals yield better fits than average variances about the detrended walk. • Endogenous structural breaks yield better forecasts than equal length alternatives. • Data fitted by power-law distributions after the first few observations. • Crossover value of 0.5 reached for samples of at least four years of daily data. [ABSTRACT FROM AUTHOR]

Abstract By employing Detrended Fluctuation Analysis (DFA), which has been established as a standard method to investigate long range correlations in nonstationary time series, we study the temporal correlations between the magnitudes of the earthquakes that occurred before the recent deadly Mexico M8.2 earthquake on 7 September 2017 in Chiapas region. Our aim here is to shed light on the origin of the following precursory phenomenon found in our previous publication (Sarlis et al., 2018): Upon considering the analysis of seismicity in the new time domain termed natural time, it was shown that the entropy change of seismicity under time reversal exhibited an important minimum almost 3 months before this major earthquake. Here the application of DFA to earthquake magnitude time series reveals that this minimum of the entropy change of seismicity is preceded as well as followed by characteristic changes of temporal correlations between earthquake magnitudes, which are quantified by the DFA exponent α. In particular, before this minimum the long range correlations breakdown to an almost random behavior possibly turning to anticorrelation (α ≤ 0. 5) while after the minimum (and before the major M8.2 earthquake) long range correlations develop with an exponent α between 0.6 and 0.7. Highlights • DFA reveals precursory changes in the correlations between earthquake magnitudes. • The origin of the entropy change before the M8.2 earthquake is investigated. • DFA exponent minimizes just before the minimum of the M8.2 precursory entropy change. [ABSTRACT FROM AUTHOR]

Abstract We present a novel approach in the research field of price–trading in stock markets. We consider the effect of trading volume (related with liquidity) on the dynamic dependence of time series, using data from the Portuguese stock market, with both listed and non-listed firms in the main index. We use detrended fluctuation analysis, with a sliding windows approach, to estimate the dynamic dependence of shares. We found two interesting and related features: firms listed in the main index have results more centred at the 0.5 level; exponents' volatility is less in the second half of the sample, after the most severe period of the Eurodebt crisis. Then, we relate this dependence with trading volume, with detrended cross-correlation analysis and the respective correlation coefficient. We found that for firms listed in the main index, some show evidence of negative correlations while for non-listed firms, most of the shares have the same results. We conclude that the lack of liquidity could be related to increased dependence. An interesting feature found is that some firms have a positive correlationship between the variables, but all those firms work with natural resources in their activities, which could mean that other factors influence their dependence patterns. Highlights • A novel approach is presented to analyse the relationship between price and trading. • We used a sliding windows approach, to evaluate dependence evolution. • We make the correlation between (dynamical) dependence and trading volume. • Results show the importance of lack of liquidity in stock market dependence. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Properties of resting alpha activity play a significant role in language ability. • Higher language scores correlated with lower α power, greater frequency flexibility. • Detrended fluctuation analysis revealed temporal correlations within α frequency. • Higher language scores correlated with longer temporal correlations. Abstract Early childhood is a period of tremendous growth in both language ability and brain maturation. To understand the dynamic interplay between neural activity and spoken language development, we used resting-state EEG recordings to explore the relation between alpha oscillations (7–10 Hz) and oral language ability in 4- to 6-year-old children with typical development (N = 41). Three properties of alpha oscillations were investigated: a) alpha power using spectral analysis, b) flexibility of the alpha frequency quantified via the oscillation's moment-to-moment fluctuations, and c) scaling behavior of the alpha oscillator investigated via the long-range temporal correlation in the alpha-amplitude time course. All three properties of the alpha oscillator correlated with children's oral language abilities. Higher language scores were correlated with lower alpha power, greater flexibility of the alpha frequency, and longer temporal correlations in the alpha-amplitude time course. Our findings demonstrate a cognitive role of several properties of the alpha oscillator that has largely been overlooked in the literature. [ABSTRACT FROM AUTHOR]

Abstract Due to global warming, extreme climate events have become an important issue, and different geographical regions have different sensitivities to climate change. Therefore, temporal and spatial variations in extreme temperature and precipitation events in Inner Mongolia were analyzed based on the daily maximum temperature, minimum temperature, and precipitation data during the period of 1960–2017. The results showed that warm extreme indices, such as SU25, TX90p, TN90p, and WSDI, significantly increased, whereas the cold extreme indices, such as FD0, TX10p, TN10p, and CSDI, significantly decreased; all indices have obvious abrupt changes based on the Mann-Kendall test; nighttime warming was higher than daytime warming. Extreme precipitation indices slightly decreased overall. All of the extreme temperature and precipitation indices had long-range correlations based on detrended fluctuation analysis (a > 0.5), thereby indicating that the extreme climate indices will maintain their current trend directions in the future. ENSO, AO, and IOD had a strong positive influence on warm extremes and a strong negative influence on cold extremes in Inner Mongolia. NCEP/NCAR and ERA-20CM reanalysis showed that strengthening anticyclone circulation, increasing geopotential height, decreasing daytime cloudiness and increasing nightime cloudiness contributed to changes in climate extremes in Inner Mongolia. Graphical abstract Unlabelled Image Highlights • Warm extreme indices significantly increased, while cold extreme indices decreased. • Nighttime warming was higher than daytime warming in Inner Mongolia. • Extreme temperature and precipitation events had long-range correlation based on DFA. • ENSO, AO, and IOD had positive (or negative) impacts on warm (or cold) extremes. [ABSTRACT FROM AUTHOR]

Abstract The purpose of the study was to compare the effects of a feedback-controlled treadmill (FeedbackTM) to a traditional fixed-speed treadmill (FixedTM) on spatiotemporal gait means, variability, and dynamics. The study also examined inter-session reliability when using the FeedbackTM. Ten young adults walked on the FeedbackTM for a 5-minute familiarization followed by a 16-minute experimental trial. They returned within one week and completed a 5-minute familiarization followed by a 16-minute experimental trial each for FeedbackTM and FixedTM conditions. Mean walking speed and step time, length, width, and speed means and coefficient of variation were calculated from all experimental conditions. Step time, length, width, and speed gait dynamics were analyzed using detrended fluctuation analysis. Mean differences between experimental trials were determined using ANOVAs and reliability between FeedbackTM sessions was determined by intraclass correlation coefficient. No difference was found in mean walking speed nor spatiotemporal variables, with the exception of step width, between the experimental trials. All mean spatiotemporal variables demonstrated good to excellent reliability between sessions, while coefficient of variation was not reliable. Gait dynamics of step time, length, width, and speed were significantly more persistent during the FeedbackTM condition compared to FixedTM, especially step speed. However, gait dynamics demonstrated fair to poor reliability between FeedbackTM sessions. When walking on the FeedbackTM, users maintain a consistent set point, yet the gait dynamics around the mean are different when compared to walking on a FixedTM. In addition, spatiotemporal gait dynamics and variability may not be consistent across separate days when using the FeedbackTM. [ABSTRACT FROM AUTHOR]

Abstract The time evolutions of transient current data for As 2 Te 3 (In) thin films were measured at different electric fields for the dc voltages of 0.001 V, 0.002 V, 0.005 V, 0.01 V, 0.1 V, 0.5 V and 1 V at room temperature (296 K). Transient current was analyzed by means of time series analysis in order to identify different conduction regimes. The maximal Lyapunov exponents for the transient currents were calculated. Positive maximal Lyapunov exponents reflected electric field dependence with positive Lyapunov exponents. Existence of a positive Lyapunov exponent means sensitive dependence on initial conditions and As 2 Te 3 (In) is known to have memory effects hence different initial conditions for each dc current measurement. Detrended Fluctuation Analysis was utilized to characterize the behavior of dc current time series which is invariant to initial conditions. Detrended Fluctuation Analysis identified three different conduction regimes with multiple conduction mechanisms. Highlights • DFA identifies three district conduction regime in As 2 Te 3 (In). • DFA clarifies competing conduction mechanisms in each regime. • DFA reveals the long range correlation properties. • The existence of an interplay between the particular mechanisms. • A crossover indicates different mechanisms with different time scales. [ABSTRACT FROM AUTHOR]

Abstract The aim of the present study was to investigate how age and sensory deprivation affect the temporal organization of CoP sway variability and the postural corrective commands during standing balance in typically developing preschoolers. A sample of 57 children aged 3–5 years participated in the study. Structural stabilometric descriptors of sample entropy (SEn), detrended fluctuation analysis (DFA), and sway density curve (SDC) analysis were employed to assess features of center of pressure sway. A force platform was used to collect center of pressure data during standing balance over 40 s in four conditions: standing on rigid and foam surfaces with eyes open and closed. The main results are as follows: (1) sample entropy decreased and DFA_coefficient increased with age, while the SDC variables remained unaltered among the 3-, 4-, and 5-year-old children; (2) as sensory conditions became more challenging, sample entropy decreased and DFA_coefficient increased, while MT and MD decreased and MD increased; age did not influence the responses to sensorial deprivation. In conclusion, 5-year-old children showed decreased variability of CoP sway during standing balance compared with the younger children, but all children used the same corrective torques to control for perturbations. More challenging sensory deprivation conditions resulted in decreased variability of postural sway, higher amplitudes and more frequent correcting torques for stabilization, but age did not influence these behaviors. [ABSTRACT FROM AUTHOR]

Abstract An experimental investigation on the self-excited spiky oscillations in a glow discharge plasma device is presented. The system showed a continuous transition from chaotic bursting to chaotic spiking states with the increase in the applied discharge voltage at a particular pressure. Below a threshold discharge voltage, the system exhibits a fixed point dynamical behaviour and with the increase in the discharge voltage bursting type oscillations appear which are identified as "fold/fold circle" type. The occurrence of the relaxation type spiky oscillations is attributed to the formation and disruption of anode double layer. The burst phases occurred intermittently and the frequency of their occurrence increased with increase in the discharge voltage and finally the oscillation turned into spiking behaviour. The intermittent oscillations manifested the scaling law of the average laminar phase with respect to the change in the discharge voltage. The scaling law of the intermittent oscillations was similar to type III intermittency. During the transition, the system changes its behaviour from long range correlation to long range anti correlation. Detrended fluctuation analysis technique has been used to identify the nature of long range correlations. We found that bursting oscillations show scale invariant, long-range correlation behaviour whereas spiking oscillations show scale invariant, long range anti correlation behaviour. Highlights • Transition from chaotic bursting to chaotic spiking behaviour is observed. • Floating potential fluctuations are intermittent in nature. • We found transition from long range correlation to long range anti correlation behaviour. • Long range correlation behaviour investigated using detrended fluctuation analysis. [ABSTRACT FROM AUTHOR]

Highlights • We hypothesized that autonomic dysfunction links depression and cardiac disease. • Polysomnography was performed in patients with depression and healthy subjects. • Fractal (alpha-1) heart rate variability correlated with beck depression inventory score. • Heart rate variability may be used as an indicator of depressive disorder. Abstract We investigated the relationship between autonomic nervous system activity during each sleep stage and the severity of depressive symptoms in patients with major depressive disorder (MDD) and healthy control subjects. Thirty patients with MDD and thirty healthy control subjects matched for sex, age, and body mass index completed standard overnight polysomnography. Depression severity was assessed using the Beck Depression Inventory (BDI). Time- and frequency-domain, and fractal HRV parameters were derived from 5-min electrocardiogram segments during light sleep, deep sleep, rapid eye movement (REM) sleep, and the pre- and post-sleep wake periods. Detrended fluctuation analysis (DFA) alpha-1 values during REM sleep were significantly higher in patients with MDD than in control subjects, and a significant correlation existed between DFA alpha-1 and BDI score in all subjects. DFA alpha-1 was the strongest predictor for the BDI score, along with REM density as a covariate. This study found that compared with controls, patients with MDD show reduced complexity in heart rate during REM sleep, which may represent lower cardiovascular adaptability in these patients, and could lead to cardiac disease. Moreover, DFA alpha-1 values measured during REM sleep may be useful as an indicator for the diagnosis and monitoring of depression. [ABSTRACT FROM AUTHOR]

• Young adults do not demonstrate diurnal variation in stride time variability (STV). • STV reliability calculated from single walking trials is fair to poor. • Improving STV reliability would improve its application and interpretation. : There is emerging evidence that gait variability outcomes provide unique insights regarding the status of an individual's locomotor control system; however, there is currently limited evidence on the within-day reliability of stride time variability (STV) outcomes, or whether they demonstrate diurnal variation, when measured during continuous, overground walking in healthy young adults. : 1) Are STV outcomes measured in the morning and afternoon during continuous, overground walking significantly different in healthy young adults? 2) What is the within-day reliability of STV outcomes measured during continuous, overground walking in healthy young adults?. : Thirty-one healthy young adults (20.8 ± 3.7 years) completed two 10-minute continuous, overground walking trials on the same day (9:00-11:00am and 3:00-5:00pm) at their preferred walking speed. Data from a waist-mounted tri-axial accelerometer were used to determine the series of consecutive stride times for each trial. : There were no significant differences between sessions for average walking speed, average stride time, or STV. The within-day reliability was excellent for average walking speed and stride time, and generally poor to fair for STV. : Healthy young adults do not appear to demonstrate diurnal variation in STV outcomes during continuous, overground walking; however, the development of a protocol to improve their reliability, as well as the establishment of normative ranges for such outcomes, would be beneficial to improve their application and interpretation in research and clinical settings. [ABSTRACT FROM AUTHOR]

Abstract Music presents a complex case of movement timing, as one to several dozen musicians coordinate their actions at short time-scales. This process is often directed by a conductor who provides a visual beat and guides the ensemble through tempo changes. The current experiment tested the ways in which audio-motor coordination is influenced by visual cues from a conductor's gestures, and how this influence might manifest in two ways: movements used to produce sound related to the music, and movements of the upper-body that do not directly affect sound output. We designed a virtual conductor that was derived from morphed motion capture recordings of human conductors. Two groups of participants (29 musicians and 28 nonmusicians, to test the generalizability of visuo-motor synchronization to non-experts) were shown the virtual conductor, a simple visual metronome, or a stationary circle while completing a drumming task that required synchronization with tempo-changing musical sequences. We measured asynchronies and temporal anticipation in the drumming task, as well as participants' upper-body movement using motion capture. Drumming results suggest the conductor generally improves synchronization by facilitating anticipation of tempo changes in the music. Motion capture results showed that the conductor visual cue elicited more structured head movements than the other two visual cues for nonmusicians only. Multiple regression analysis showed that the nonmusicians with less rigid movement and high anticipation had lower asynchronies. Thus, the visual cues provided by a conductor might serve to facilitate temporal anticipation and more synchronous movement in the general population, but might also cause rigid ancillary movements in some non-experts. Highlights • Visual cues with human-like kinematics improve synchronization with musical stimuli. • This benefit manifests as higher temporal anticipation and lower asynchronies. • Non-experts move their heads more than experts when synchronizing with music. • Rigid motion induced by visual cues interferes with synchronization in non-experts. [ABSTRACT FROM AUTHOR]

Based on the detrended fluctuation analysis (DFA), we address the problem of recognizing the electrical activity of the brain associated with movements of the right arm and the imagination of this procedure. In experiments with untrained volunteers, we show the possibility to distinguish between the related EEG patterns. We also demonstrate significant distinctions in the scaling features between background records and EEG signals associated with mental intentions that can be transformed to control commands for brain–computer interfaces. This effect is observed in many EEG channels, although individual features of subjects provided various distributions of brain areas with the most prominent differences. [ABSTRACT FROM AUTHOR]

This study empirically compares the development of China’s interest rate market in the past decade. The yield of Shibor market from 2006 to 2016 showed anti-persistence, and the later five-year trend was stronger than that in the previous half period, indicating a significantly growing trend of China’s interest rate market. We perform a comparative analysis of detrended fluctuation analysis (DFA), detrending moving average (DMA) algorithm, and the maximum overlap wavelet transform (DFA-MODWT) using grey and original sequences. The autoregressive integrated moving average model is adopted to generate set-point H sequences. Results illustrated that 1) for original sequences, both the DFA-MODWT and DMA ( θ = 0 . 5 ) shows outstanding performances when the set Hurst exponents are 0.6 and 0.7 and 0.8 and 0.9, respectively. 2) For grey sequences, G-DFA performs best among the three methods, followed by G-DMA and G-DFA-MODWT. 3) The long-range correlation of the original sequences is slightly lower than the set values. On the contrary, the long-range correlation of grey sequences is much higher than the set values. Thus, the grey sequence exerts a strong aggregation effect as it can accumulate the trend of the sequence. Our results show that the grey sequence can determine the regular pattern from the messy sequence. However, grey sequences tend to be overly analyzed and misleading when large volumes of data are involved. Empirical studies confirm that China’s interest rate market remains an inefficient market. [ABSTRACT FROM AUTHOR]

In this study, we explored the fractal dimension (FD; a measure of signal complexity) of 28 EEG channels with positive and negative emotional states. The EEG of participants and their ECG were registered while watching short video clips that induced fear, disgust, humour, or neutral emotions. In order to better understand the nature of these emotions, the Higuchi FD of EEG segments and the heart rate variability (HRV) of the ECG associated with each emotion were obtained. Our results exhibited similar patterns of results with both measures. Humour elicited the highest FD scores in most EEG channels and the highest HRV, while fear, among all emotions, produced the lowest scores in both measures. These results may contribute to the understanding of the relationship between cortical and heart dynamics and their role on emotion perception. [ABSTRACT FROM AUTHOR]

The analysis of stock markets’ behaviour remains a very interesting issue, because it can give investors information about where to apply their money. In this context, a dynamic analysis of 18 Eastern European stock markets is performed, using a sliding windows detrended fluctuation analysis, which will fill a gap in the literature with the inclusion in this study of a larger sample. The results show that most indices are distant from the absence of long-range dependencies, which could be seen as inefficiency. Nevertheless, some countries show a decrease in dependence levels over time, namely the Hungarian, Czech and Polish indices. When compared with other more developed markets, these and Latvia are the only ones to show similar behaviour, regarding the existence of long-range dependencies. This could be related to more developed economic structures as well as the financial system, probably associated with joining the European Union. However, other current European Union members, such as Bulgaria, Slovenia and Lithuania do not show this pattern. [ABSTRACT FROM AUTHOR]

Abstract State estimation is a widely adopted soft sensing technique that incorporates predictions from an accurate model of the process and measurements to provide reliable estimates of unmeasured variables. The reliability of such estimators is threatened by measurement related challenges and model inaccuracies. In this article, a method for benchmarking of state estimation techniques is proposed. This method can be used to quantify the performance and hence reliability of an estimator. The Hurst exponents of a posteriori filtering errors are analyzed to characterize a benchmark (minimum mean squared error) estimator, similar to the minimum variance control benchmark developed for control loops. A distance metric is then used to quantify the extent of deviation of an estimator from the benchmark. The proposed technique is developed for linear systems and extended to non-linear systems with single as well as multiple measurable variables. Simulation studies are carried out with Kalman based as well as Monte Carlo based estimators whose computational details are significantly different. Results reveal that the technique serves as a tool that can quantify the performance and assess the reliability of a state estimator. The strengths and limitations of the proposed technique are discussed with guidelines on applications and deployment of the technique in a real life system. Highlights • State estimation is a widely used soft sensing tool. • The performance of an estimator is dependent on modeling accuracy and approximations. • A method for benchmarking the performance of estimators along with a measure of performance with respect to the benchmark are proposed. • The method is independent of the nature of the system and computational details of the estimator. [ABSTRACT FROM AUTHOR]

Structure health monitoring of composite structures is essential for safety assurance and attracted more attention in recent years. In this paper, a low velocity impact (LVI) monitoring system based on detrended fluctuation analysis (DFA) and centroid localization algorithm is proposed. First, the distance from the LVI to neighboring FBG sensors can be estimated by analyzing the Hurst exponents calculated from response signal of LVI with different distance to monitoring FBG sensors by DFA. Then, the LVI location can be determined by region identification algorithm and centroid localization algorithm. To verify the performance of the proposed LVI monitoring system, a LVI localization system using optical fiber Bragg grating (FBG) sensor was constructed and validated on a CFRP plate with 240 mm*240 mm*3 mm experiment area. The experiment results showed that the average error of localization was 31.5 mm. The proposed localization method based on DFA and centroid localization algorithm demonstrates an effective means of LVI localization. [ABSTRACT FROM AUTHOR]

In recent years, complex models of cardiac regulation have integrated heart rate variability (HRV) as a measure of the cardiac autonomic activity during exercise. Using detrended fluctuation analysis (DFA) technique, the present study examines the influence of cycling cadence and exercise duration on non-linear dynamics of HRV. Sixteen trained cyclists performed a 60-minute exercise bout at 90% of the individual anaerobic threshold on a bicycle ergometer. Cadence was changed every 10 min (90-120-60-120-60-90 rpm). Heart rate (HR) and RR-intervals were recorded continuously during exercise. HRV time domain measures (meanRR, SDNN) and correlation properties were analyzed using short-term scaling exponent alpha1 of DFA. Moreover, blood lactate (La) and rating of perceived exertion (RPE) were recorded at regular intervals at the end of condition. HR, La and RPE increased significantly at 120 rpm compared to 60 rpm. In contrast, all analyzed HRV parameters (meanRR, SDNN, DFA-alpha1) showed a significant decrease during cycling at 120 rpm compared to 60 rpm. The comparison of the first and last 10 min with the same cadence indicates a significant increase in HR and RPE, but also a significant decrease in all analyzed HRV measures. The decrease of HRV values over time and in relation to the increase in cadence indicates a decrease in the overall variability as well as a reduction in complexity of the RR-interval-fluctuations due to the increased organismic demands. Therefore, the decrease of DFA-alpha1 might be associated with a withdrawal of the organismic system aiming at the maintenance of the homeostasis under the control of the central nervous system. In this context, non-linear HRV analyses provide a more systemic view of cardiac regulation during exercise. [ABSTRACT FROM AUTHOR]

The wind speed persistence is an important factor in the assessment of wind energy potential. In this paper, we explore the persistence of Mean Wind Speed (MWS) with many years of record using Detrended Fluctuation Analysis (DFA) over China. The results illustrate that there exist irregular high-frequency fluctuations for daily MWS anomaly records. Long-term persistence of MWS is found for all meteorological observed sites. We also make some numerical tests in order to verify the significance of long-term persistence by shuffling the data records many times. These facts prove that the MWS anomaly records have long-term persistence over all the stations in China. The mean value 0.64 in DFA-exponents for all stations over China is also obviously higher than the value 0.53 according to interval threshold of 95% confidence level after shuffling the MWS records many times. In addition, the values of scaling exponent vary from station to station over China. Long-term persistence of MWS in spatial distributions seems to be downward trends from east to west China. Many factors may affect long-term persistence of MWS such as southwest monsoon, Tibetan Plateau landform and atmosphere–ocean–land interaction and so on. Possible physical mechanism need further analysis in the future. [ABSTRACT FROM AUTHOR]

Autoregressive models are commonly used for modeling time-series from nature, economics and finance. This work explored simple autoregressive AR ( p ) models to remove long-term trends in detrended fluctuation analysis (DFA). Crude oil prices and bitcoin exchange rate were considered, with the former corresponding to a mature market and the latter to an emergent market. Results showed that AR ( p ) -based DFA performs similar to traditional DFA. However, the former DFA provides information on stability of long-term trends, which is valuable for understanding and quantifying the dynamics of complex time series from financial systems. [ABSTRACT FROM AUTHOR]

We study the temporal correlations in the evolution of the order parameter ϕ t for the Vicsek model with vectorial noise by estimating its Hurst exponent H with detrended fluctuation analysis (DFA). We present results on this parameter as a function of noise amplitude η introduced in simulations. We also compare with well known order–disorder phase transition for that same noise range. We find that – regardless of detrending degree – H spikes at the known coexistence noise for phase transition, and that this is due to nonstationarities introduced by the transit of the system between two well defined states with lower exponents. We statistically support this claim by successfully synthesizing equivalent cases derived from a transformed fractional Brownian motion (TfBm). [ABSTRACT FROM AUTHOR]

Basketball is one of the favourite sports in the United States and NBA the most popular basketball league in the world, followed not only by Americans but by fans everywhere. At present, people are increasingly interested in betting on sports in general and on basketball in particular. So analysis of trends in the results could be important for gamblers. In this paper, detrended fluctuation analysis is applied to 28 NBA teams, in order to understand if their results have (or not) any kind of memory. Our results show that all the teams are persistent in their results. Nevertheless, some teams have higher persistence than others, which could be important for gamblers in deciding how to bet. [ABSTRACT FROM AUTHOR]

Background: Aging and Parkinson's disease are often associated with impaired postural control. Providing extrinsic feedback via vibrotactile sensation could supplement intrinsic feedback to maintain postural control.Research Question: We investigated the postural control response to vibrotactile feedback provided at the trunk during challenging stance conditions in older adults at high fall risk and individuals with Parkinson's disease compared to healthy older adults.Methods: Nine older adults at high fall risk, 9 persons with Parkinson's disease and 10 healthy older adults performed 30s quiet standing on a force platform under five challenging stance conditions with eyes open/closed and standing on firm/foam surface with feet together, each with and without vibrotactile feedback. During vibrotactile feedback trials, feedback was provided when participants swayed >10% over the center of their base of support. Participants were instructed vibrations would be in response to their movement. Magnitude of postural sway was estimated using center of pressure path length, velocity, and sway area. Dynamics of individuals' postural control was evaluated using detrended fluctuation analysis.Results: Results showed that vibrotactile feedback induced a change in postural control dynamics among persons with Parkinson's disease when standing with intact intrinsic visual input and altered intrinsic somatosensory input, but there was no change in sway magnitude. However, use of vibrotactile feedback did not significantly alter dynamics of postural control in older adults with high risk of falling or reduce the magnitude of sway.Significance: Considering the effects of vibrotactile feedback were dependent on the population and stance condition, designing an optimal therapeutic regimen for balance training should be carefully considered and be specific to a target population. Furthermore, our results suggest that explicit instructions on how to respond to the vibrotactile feedback could affect training outcome. [ABSTRACT FROM AUTHOR]

Up to 40% of patients treated for rectal cancer suffer from therapy-related symptoms. Innervation injury is one of the suggested pathomechanisms of those symptoms hence the development of a valid, non-invasive tool for the assessment of neural systems is crucial. The aim of this work is to study the fractal properties of the surface electromyography signals obtained from patients suffering from rectal cancer. The anal sphincter activity was investigated for the group of 15 patients who underwent surgical treatment. Multifractal detrended fluctuation analysis was implemented to analyze the data, obtained at four different stages: one before treatment and three times after the surgery. The results from the standard detrended fluctuation analysis and empirical mode decomposition methods are presented and compared. The statistically significant differences between the stages of treatment were identified for the selected spectral parameters: width and maximum of the spectrum. [ABSTRACT FROM AUTHOR]

Prolonged walking could alter postural control leading to an increased risk of falls in older adults. The aim of this study was to determine the effect of level and uphill prolonged walking on the postural control of older adults. Sixteen participants (64 ± 5 years) attended 3 visits. Postural control was assessed during quiet standing and the limits of stability immediately pre, post and post 15 min rest a period of 30 min walking on level and uphill (5.25%) gradients on separate visits. Each 30 min walk was divided into 3 10 min blocks, the limits of stability were measured between each block. Postural sway elliptical area (PRE: 1.38 ± 0.22 cm 2 , POST: 2.35 ± 0.50 cm 2 , p = .01), medio-lateral (PRE: 1.33 ± 0.03, POST: 1.40 ± 0.03, p = .01) and anterio-posterior detrended fluctuation analysis alpha exponent (PRE: 1.43 ± 0.02, POST: 1.46 ± 0.02, p = .04) increased following walking. Medio-lateral alpha exponent decreased between post and post 15 min’ rest (POST: 1.40 ± 0.03, POST15: 1.36 ± 0.03, p = .03). Forward limits of stability decreased between the second walking interval and post 15 min’ rest (Interval 2: 28.1 ± 1.6%, POST15: 25.6 ± 1.6%, p = .01) and left limits of stability increased from pre-post 15 min’ rest (PRE: 27.7 ± 1.2%, POST15: 29.4 ± 1.1%, p = .01). The neuromuscular alterations caused by prolonged walking decreased the anti-persistence of postural sway and altered the limits of stability in older adults. However, 15 min’ rest was insufficient to return postural control to pre-exercise levels. [ABSTRACT FROM AUTHOR]

This study investigates the nonlinear dynamics of the stratospheric ozone layer at Pakistan atmospheric region. Ozone considered now the most important issue in the world because of its diverse effects on earth biosphere, including human health, ecosystem, marine life, agriculture yield and climate change. Therefore, this paper deals with total monthly time series data of stratospheric ozone over the Pakistan atmospheric region from 1970 to 2013. Two approaches, basic statistical analysis and Fractal dimension (D) have adapted to study the nature of nonlinear dynamics of stratospheric ozone level. Results obtained from this research have shown that the Hurst exponent values of both methods of fractal dimension revealed an anti-persistent behavior (negatively correlated), i.e. decreasing trend for all lags and Rescaled range analysis is more appropriate as compared to Detrended fluctuation analysis. For seasonal time series all month follows an anti-persistent behavior except in the month of November which shown persistence behavior i.e. time series is an independent and increasing trend. The normality test statistics also confirmed the nonlinear behavior of ozone and the rejection of hypothesis indicates the strong evidence of the complexity of data. This study will be useful to the researchers working in the same field in the future to verify the complex nature of stratospheric ozone. [ABSTRACT FROM AUTHOR]

Complex systems applications in human movement sciences have increased our understanding of emergent coordination patterns between athletes. In the current study, we take a novel step and propose that movement coordination between athletes is a multiscale phenomenon. Specifically, we investigated so-called "complexity matching" of performance measured in the context of rowing. Sixteen rowers participated in two sessions on rowing ergometers: One individual session of 550 strokes and one dyadic session of 550 strokes side-by-side with a team member. We used evenly-spaced detrended fluctuation analysis (DFA) to calculate the complexity indices (DFA exponents) of the force-peak interval series for each rower in each session. The DFA exponents between team members were uncorrelated in the individual sessions (r = 0.06), but were strongly and significantly correlated when team members rowed together (r = 0.87). Furthermore, we found that complexity matching could not be attributed to the rowers mimicking or locally adapting to each other. These findings contribute to the current theoretical understanding of coordination dynamics in sports. [ABSTRACT FROM AUTHOR]

Detrended Fluctuation Analysis (DFA) has become a widely-used tool to examine the correlation structure of a time series and provided insights into neuromuscular health and disease states. As the popularity of utilizing DFA in the human behavioral sciences has grown, understanding its limitations and how to properly determine parameters is becoming increasingly important. DFA examines the correlation structure of variability in a time series by computing α , the slope of the log S D – log n diffusion plot. When using the traditional DFA algorithm, the timescales, n , are often selected as a set of integers between a minimum and maximum length based on the number of data points in the time series. This produces non-uniformly distributed values of n in logarithmic scale, which influences the estimation of α due to a disproportionate weighting of the long-timescale regions of the diffusion plot. Recently, the evenly spaced DFA and evenly spaced average DFA algorithms were introduced. Both algorithms compute α by selecting k points for the diffusion plot based on the minimum and maximum timescales of interest and improve the consistency of α estimates for simulated fractional Gaussian noise and fractional Brownian motion time series. Two issues that remain unaddressed are (1) how to select k and (2) whether the evenly-spaced DFA algorithms show similar benefits when assessing human behavioral data. We manipulated k and examined its effects on the accuracy, consistency, and confidence limits of α in simulated and experimental time series. We demonstrate that the accuracy and consistency of α are relatively unaffected by the selection of k . However, the confidence limits of α narrow as k increases, dramatically reducing measurement uncertainty for single trials. We provide guidelines for selecting k and discuss potential uses of the evenly spaced DFA algorithms when assessing human behavioral data. [ABSTRACT FROM AUTHOR]

Heart rate asymmetry is a phenomenon by which the accelerations and decelerations of heart rate behave differently, and this difference is consistent and unidirectional, i.e. in most of the analyzed recordings the inequalities have the same directions. So far, it has been established for variance and runs based types of descriptors of R R intervals time series. In this paper we apply the newly developed method of Asymmetric Detrended Fluctuation Analysis, which so far has mainly been used with economic time series, to the set of 420 stationary 30 min time series of R R intervals from young, healthy individuals aged between 20 and 40. This asymmetric approach introduces separate scaling exponents for rising and falling trends. We systematically study the presence of asymmetry in both global and local versions of this method. In this study global means “applying to the whole time series” and local means “applying to windows jumping along the recording”. It is found that the correlation structure of the fluctuations left over after detrending in physiological time series shows strong asymmetric features in both magnitude, with α + < α − , where α + is related to heart rate decelerations and α − to heart rate accelerations, and the proportion of the signal in which the above inequality holds. A very similar effect is observed if asymmetric noise is added to a symmetric self-affine function. No such phenomena are observed in the same physiological data after shuffling or with a group of symmetric synthetic time series. [ABSTRACT FROM AUTHOR]

Both sub-prime and Eurozone debt crisis problems caused severe financial crisis, which affected European markets in general, but particularly the banking sector. The continuous devaluation of bank shares in the financial sector caused a great decrease in market capitalization, and in citizen and investor confidence. Panic among investors led them to sell shares, while other agents took the opportunity to buy them. Therefore, the study of bank shares is important, particularly of their efficiency. In this paper, adopting a sliding windows detrended fluctuation approach, we analyse the efficiency concept dynamically with 63 European banks (both in and outside the Eurozone). The main results show that the crisis had an effect on changing the efficiency pattern. [ABSTRACT FROM AUTHOR]

We have carried out a detailed study of scaling region using detrended fractal analysis test by applying different forcing likewise noise, sinusoidal, square on the floating potential fluctuations acquired under different pressures in a DC glow discharge plasma. The transition in the dynamics is observed through recurrence plot techniques which is an efficient method to observe the critical regime transitions in dynamics. The complexity of the nonlinear fluctuation has been revealed with the help of recurrence quantification analysis which is a suitable tool for investigating recurrence, an ubiquitous feature providing a deep insight into the dynamics of real dynamical system. An informal test for stationarity which checks for the compatibility of nonlinear approximations to the dynamics made in different segments in a time series has been proposed. In case of sinusoidal, noise, square forcing applied on fluctuation acquired at P = 0.12 mbar only one dominant scaling region is observed whereas the forcing applied on fluctuation (P = 0.04 mbar) two prominent scaling regions have been explored reliably using different forcing amplitudes indicating the signature of crossover phenomena. Furthermore a persistence long range behavior has been observed in one of these scaling regions. A comprehensive study of the quantification of scaling exponents has been carried out with the increase in amplitude and frequency of sinusoidal, square type of forcings. The scalings exponent is envisaged to be the roughness of the time series. The method provides a single quantitative idea of the scaling exponent to quantify the correlation properties of a signal. [ABSTRACT FROM AUTHOR]

In this paper, a generalized method of detrended fluctuation analysis (DFA) is proposed as a new measure to assess the complexity of a complex dynamical system such as stock market. We extend DFA and local scaling DFA to higher moments such as skewness and kurtosis (labeled SMDFA and KMDFA), so as to investigate the volatility scaling property of financial time series. Simulations are conducted over synthetic and financial data for providing the comparative study. We further report the results of volatility behaviors in three American countries, three Chinese and three European stock markets by using DFA and LSDFA method based on higher moments. They demonstrate the dynamics behaviors of time series in different aspects, which can quantify the changes of complexity for stock market data and provide us with more meaningful information than single exponent. And the results reveal some higher moments volatility and higher moments multiscale volatility details that cannot be obtained using the traditional DFA method. [ABSTRACT FROM AUTHOR]