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We investigated the fractal characteristics of geophysical logs in gas hydrate-bearing media to improve quantitative reservoir analyses. We implemented Detrended Fluctuation Analysis (DFA) and Multifractal Detrended Fluctuation Analysis (MFDFA) to analyze the scale-invariant features of log fluctuations, which are indicative of varying gas hydrate-saturated and gas hydrate-free zones in the Krishna-Godavari basin, India. Our findings indicate that fractal parameters such as scaling exponent (훼) is higher (훼 > 1.189) in gas hydrate zones as compared to non-hydrate zones (where, 훼 < 1.005). The relationship between 훼 and gas hydrate saturation suggests long-range correlations and self-similarity in log responses, emphasizing vertical heterogeneity. Subsequently, we performed scaling window-based DFA, which revealed clearer signatures of hydrate presence affecting the average values of resistivity and p-wave velocity for a typical window size. The associated fractal parameters, such as 훼 and H, showed a positive correlation with the increase in gas hydrate saturation. Furthermore, MFDFA revealed both vertical and spatial heterogeneity in these sedimentary systems, with a shift in singularity spectra towards higher scaling exponents (ℎ푞​) in correlation with increase in gas hydrate saturation. This study underscores the utility of statistical approaches in providing reliable insights into subsurface gas hydrate distribution patterns, setting the stage for future gas hydrate sediment investigations. [ABSTRACT FROM AUTHOR]

The short-term scaling exponent of detrended fluctuation analysis (DFA-a1) of heart rate variability may be a helpful tool to assess autonomic balance as a prelude to daily, individualized training. For this concept to be useful, between-session reliability should be acceptable. The aim of this study was to explore the reliability of DFA-a1 during a low-intensity exercise session in both a non-fatigued and a fatigued condition in healthy males and females. Ten participants completed two sessions with each containing an exhaustive treadmill ramp protocol. Before and after the fatiguing ramp, a standardized submaximal low-intensity exercise bout was performed during which DFA-a1, heart rate, and oxygen consumption (VO2) were measured. We compared between-session reliability of all metrics prior to the ramps (i.e., non-fatigued status) and after the first ramp (i.e., fatigued status). Intraclass correlation coefficients (ICC) with 95% confidence intervals (CI), the standard error of measurement, and the smallest worthwhile change (SWC) were determined. The ICC and SWC pre fatiguing ramp were 0.85 (95% CI 0.39–0.96) and 5.5% for DFA-a1, 0.85 (0.38–0.96) and 2.2% for heart rate, and 0.84 (0.31–0.96) and 3.1% for VO2. Post fatiguing ramp, the ICC and SWC were 0.55 (0.00–0.89) and 7.9% for DFA-a1, 0.91 (0.62–0.98) and 1.6% for heart rate, and 0.80 (0.17–0.95) and 3.0% for VO2. DFA-a1 shows generally acceptable to good between-session reliability with a SWC of 0.06 and 0.07 (5.5–7.9%) during non-fatigued and fatigued conditions. This suggests that this metric may be useful to inform on training readiness. [ABSTRACT FROM AUTHOR]

The kinetics of an ion channel are classically understood as a random process. However, studies have shown that in complex ion channels, formed by multiple subunits, this process can be deterministic, presenting long-term memory. Staphylococcus aureus α-hemolysin (α-HL) is a toxin that acts as the major factor in Staphylococcus aureus virulence. α-HL is a water-soluble protein capable of forming ion channels into lipid bilayers, by insertion of an amphipathic β-barrel. Here, the α-HL was used as an experimental model to study memory in ion channel kinetics. We applied the approximate entropy (ApEn) approach to analyze randomness and the Detrended Fluctuation Analysis (DFA) to investigate the existence of long memory in α-HL channel kinetics. Single-channel currents were measured through experiments with α-HL channels incorporated in planar lipid bilayers. All experiments were carried out under the following conditions: 1 M NaCl solution, pH 4.5; transmembrane potential of + 40 mV and temperature 25 ± 1 °C. Single-channel currents were recorded in real-time in the memory of a microcomputer coupled to an A/D converter and a patch-clamp amplifier. The conductance value of the α-HL channels was 0.82 ± 0.0025 nS (n = 128). The DFA analysis showed that the kinetics of α-HL channels presents long-term memory ( DFA α = 0.63 ± 0.04). The ApEn outcomes showed low complexity to dwell times when open (ApEno = 0.5514 ± 0.28) and closed (ApEnc = 0.1145 ± 0.08), corroborating the results of the DFA method. [ABSTRACT FROM AUTHOR]

The control of voluntary movements is a dual structure consisting of cognitive and physical controls; cognitive control, unlike physical control requires attentional resources. Various voluntary movements can be performed by combining cognitive and physical controls. Body movements depending on attentional resources are performed using cognitive control; these movements fluctuate with white noise and their fluctuations gradually change to one-over-f fluctuation as the dependence on attentional resources decreases. Characters are handwriting processes in voluntary movement. This study focused on the relationship between a repetitive handwriting process and attentional resources allocated to it. The attention resources allocated to handwriting processes depend on how challenging the task is. Moreover, the difficulty of a handwriting task is determined by the complexity of the shape of the handwritten characters, and the stroke counts are one indicator of this. Therefore, we focused on three Chinese kanji characters with different stroke counts. Attentional resources can be identified by tapping movements concurrently with writing movements and comparing the result. An experiment was conducted for 6 days for each of the three Chinese kanji characters, with 25 subjects who were familiar with the Chinese kanji character. We investigated fluctuations in the six temporal handwriting elements defined within each Chinese kanji character handwriting process. In the analysis, six-dimensional temporal handwriting elements were reduced to three dimensions using principal component analysis. Furthermore, detrended fluctuation analysis was applied to the three-dimensional principal components. In this study, we examine the effectiveness of principal component analysis for the analysis of multidimensional data. Furthermore, we discussed the relationship between handwriting task difficulties and temporal handwriting elements using local scaling indices based on detrended fluctuation analysis. [ABSTRACT FROM AUTHOR]

Previous studies showed that scale-free structures and long-range temporal correlations are ubiquitous in physiological signals (e.g., electroencephalography). This is supposed to be associated with optimized information processing in human brain. The instantaneous alpha frequency (IAF) (i.e., the instantaneous frequency of alpha band of human EEG signals) may dictate the resolution at which information is sampled and/or processed by cortical neurons. To the best of our knowledge, no research has examined the scale-free dynamics and potential functional significance of IAF. Here, through three studies (Study 1: 25 participants; Study 2: 82 participants; Study 3: 26 participants), we investigated the possibility that time series of IAF exhibit scale-free property through maximum likelihood based detrended fluctuation analysis (ML-DFA). This technique could provide the scaling exponent (i.e., DFA exponent) on the basis of presence of scale-freeness being validated. Then the test–retest reliability (Study 1) and potential influencing factors (Study 2 and Study 3) of DFA exponent of IAF fluctuations were investigated. Firstly, the scale-free property was found to be inherent in IAF fluctuations with fairly high test–retest reliability over the parietal-occipital region. Moreover, the task manipulations could potentially modulate the DFA exponent of IAF fluctuations. Specifically, in Study 2, we found that the DFA exponent of IAF fluctuations in eye-closed resting-state condition was significantly larger than that in eye-open resting-state condition. In Study 3, we found that the DFA exponent of IAF fluctuations in eye-open resting-state condition was significantly larger than that in visual n-back tasks. The DFA exponent of IAF fluctuations in the 0-back task was significantly larger than in the 2-back and 3-back tasks. The results in studies 2 and 3 indicated that: (1) a smaller DFA exponent of IAF fluctuations should signify more efficient online visual information processing; (2) the scaling property of IAF fluctuations could reflect the physiological arousal level of participants. [ABSTRACT FROM AUTHOR]

Studies highlight the usage of non-linear time series analysis of heart rate variability (HRV) using the short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA-alpha1) during exercise to determine aerobic and anaerobic thresholds. The present study aims to further verify this approach in women. Gas exchange and HRV data were collected from 26 female participants with different activity levels. Oxygen uptake (VO2) and heart rate (HR) at first (VT1) and second ventilatory thresholds (VT2) were compared with DFA-alpha1-based thresholds 0.75 (HRVT1) and 0.50 (HRVT2). Results: VO2 at VT1 and VT2 were 25.2 ml/kg/min (± 2.8) and 31.5 ml/kg/min (± 3.6) compared with 26.5 ml/kg/min (± 4.0) and 31.9 ml/kg/min (± 4.5) for HRVT1 and HRVT2, respectively (ICC3,1 = 0.77, 0.84; r = 0.81, 0.86, p < 0.001). The mean HR at VT1 was 147 bpm (± 15.6) and 167 bpm (± 12.7) for VT2, compared with 152 bpm (± 15.5) and 166 bpm (± 13.2) for HRVT1 and HRVT2, respectively (ICC3,1 = 0.87, 0.90; r = 0.87, 0.90, p < 0.001). Bland–Altman analysis for VT1 vs. HRVT1 showed a mean difference of − 1.3 ml/kg/min (± 2.4; LoA: 3.3, − 6.0 ml/kg/min) for VO2 and of − 4.7 bpm (± 7.8; LoA: 10.6, − 20.0 bpm) for HR. VT2 vs. HRVT2 showed a mean difference of − 0.4 ml/kg/min (± 2.3; LoA: 4.1, − 4.9 ml/kg/min) for VO2 and 0.5 bpm (± 5.7; LoA: 11.8, − 10.8 bpm) for HR. DFA-alpha1-based thresholds showed good agreement with traditionally used thresholds and could be used as an alternative approach for marking organismic transition zones for intensity distribution in women. [ABSTRACT FROM AUTHOR]

The short-term scaling exponent of detrended fluctuation analysis (DFAα1) applied to interbeat intervals may provide a method to identify ventilatory thresholds and indicate systemic perturbation during prolonged exercise. The purposes of this study were to (i) identify the gas exchange threshold (GET) and respiratory compensation point (RCP) using DFAα1 values of 0.75 and 0.5 from incremental exercise, (ii) compare DFAα1 thresholds with DFAα1 measures during constant-speed running near the maximal lactate steady state (MLSS), and (iii) assess the repeatability of DFAα1 between MLSS trials. Twelve runners performed an incremental running test and constant-speed running 5% below, at, and 5% above the MLSS, plus a repeat trial at MLSS. During 30-min running trials near MLSS, DFAα1 responses were variable (i.e., 0.27–1.24) and affected by intensity (p = 0.031) and duration (p = 0.003). No difference in DFAα1 was detected between MLSS trials (p = 0.597). In the early phase (~ 8 min), DFAα1 measures at MLSS (0.71 [0.13]) remained higher than the DFAα1 identified at RCP from the incremental test (0.57 [0.13]; p = 0.024). In addition, following ~ 18 min of constant speed running at MLSS, DFAα1 measures (0.64 [0.14]) remained higher than 0.5 (p = 0.011)—the value thought to demarcate the boundaries between heavy and severe exercise intensities. Accordingly, using fixed DFAα1 values associated with the RCP from incremental exercise to guide constant-speed exercise training may produce a greater than expected exercise intensity, however; the dependency of DFAα1 on intensity and duration suggest its potential utility to quantify systemic perturbations imposed by continuous exercise. [ABSTRACT FROM AUTHOR]

The droplet behavior of welding is chaotic and fractal, and thus is significant for diagnosis of weld quality. To study the long-range correlation of fractals, detrended fluctuation analysis (DFA) is introduced for current and voltage signals. The DFA curve obviously has crossover and can be expressed by a two exponent model, including a short-term exponent (α1) at small scale and a long-term exponent (α2) at large scale. However, the relationship between the weld seam width and the two exponent model is not obviously linear. A high-dimensional feature is generated on basis of all points of the DFA curve, t-distributed stochastic neighbor embedding is used for dimension reduction. Then, a low-dimensional feature combined with support vector machine is used to predict weld seam width, which achieves higher classification accuracy than the two exponent model. This study provides a new attempt about the chaotic and fractal characteristics in welding. [ABSTRACT FROM AUTHOR]

Climate change has been a significant subject in recent years all around the world. Statistical analysis of climatic parameters such as rainfall can investigate the actual status of the atmosphere. As a result, this study aimed to look at the pattern of mean annual rainfall in India from 1901 to 2016, considering 34 meteorological subdivisions. The Mann–Kendall (MK) test, Modified Mann–Kendall (MMK) test, Bootstrapped MK (BMK) test, and Innovative Trend Analysis (ITA) were used to find trends in yearly rainfall time-series results. Rainfall forecasting was evaluated using detrended fluctuation analysis (DFA). Because the research comprised 34 meteorological subdivisions, it may be challenging to convey the general climatic conditions of India in a nutshell. The MK, MMK, and BMK tests showed a significant (p < 0.01 to p < 0.1) negative trend in 9, 8, and 9 sub-divisions, respectively. According to the ITA, a negative trend was found in 17 sub-divisions, with 9 sub-divisions showing a significance level of 0.01 to 0.1. The ITA outperformed the other three trend test techniques. The results of DFA showed that 20 sub-divisions would decrease in future rainfall, suggesting that there was a link between past and future rainfall trends. Results show that highly negative or decreasing rainfall trends have been found in broad regions of India, which could be related to climate change, according to the results. ITA and DFA techniques to discover patterns in 34 sub-divisions across India have yet to be implemented. In developing management plans for sustainable water resource management in the face of climate change, this research is a valuable resource for climate scientists, water resource scientists, and government officials. [ABSTRACT FROM AUTHOR]

The dynamical complexities of the Nigerian stock market have been largely uninvestigated. In this study, we analysed the return price of financial stocks on Nigerian Stock Exchange for chaotic behaviour. Our analysis was conducted for the period 2000–2015, as well as for three distinct periods covering the pre-crisis, crisis and post-crisis period of 2008. Fractal anlaysis (detrended fluctuation analysis and rescale range), entropy (Kolmogorov and permutation), recurrence quantification analysis (determinism and longest diagonal line) and Lyapunov exponent (Rosenstein and Eckmann) methods were used in the investigation. Results showed that the return prices of six financial stocks exhibit behaviour associated with random noise and chaos. The stocks were found to be more efficient post-crisis than during the pre-crisis period. Return prices post-crisis were found to be more chaotic. [ABSTRACT FROM AUTHOR]

Ion channels are transport proteins present in the lipid bilayers of biological membranes. They are involved in many physiological processes, such as the generation of nerve impulses, hormonal secretion, and heartbeat. Conformational changes in the ion channel-forming protein allow the opening or closing of pores to control the ionic flux through the cell membranes. The opening and closing of the ion channel have been classically treated as a random kinetic process, known as a Markov process. Here the time the channel remains in a given state is assumed to be independent of the condition it had in the previous state. More recently, however, several studies have shown that this process is not random but a deterministic one, where both the open and closed dwell-times and the ionic current flowing through the channel are history-dependent. This property is called long memory or long-range correlation. However, there is still much controversy regarding how this memory originates, which region of the channel is responsible for this property, and which models could best reproduce the memory effect. In this article, we provide a review of what is, where it is, its possible origin, and the mathematical methods used to analyze the long-term memory present in the kinetic process of ion channels. [ABSTRACT FROM AUTHOR]

The driving mechanism of Arctic amplification (AA) is so complex that no consistent and definitive conclusion has been formed yet. In particular, the internally and externally driven trends of AA have not been distinguished using observation-based methods. Given that the Arctic is more sensitive than other regions to anthropogenic greenhouse gas increases and other external forcings, we focus on separating anthropogenically driven trends from the Arctic surface air temperature (SAT) changes during 1979–2017 to quantify the contribution of anthropogenic effects on AA, with detection and attribution converted to probability distribution functions. Results indicate that the Arctic coast of the Siberian Great Plains, from the Barents Sea to the Kara Sea and eastward to the Bering Strait, has been warming most significantly, and is mainly dominated by anthropogenically driven trends. From 1979 to 2017, the minimum anthropogenically driven warming in most parts of the Arctic Ocean exceeds 2℃, especially the Kara Sea area, where the anthropogenically driven warming is significant, reaching 4℃. In addition, the minimum anthropogenic contributions exceed 60% in most parts of the Arctic Circle and are more than 80% of the warming trend in (75–90° N, 150–180° W). In 140° W–140° E Arctic region, the anthropogenically driven trend is the most remarkable, at 0.82℃ / decade, accounting for 84.5% of the measured warming trend. Meanwhile, the anthropogenically driven trend accelerates most rapidly in this area (0–140° W, 60–90° N). [ABSTRACT FROM AUTHOR]

Summary : Among 1299 older adults with 24-h Holter monitoring data at baseline, followed for approximately 15 years, 190 incident hip fractures occurred. Increased heart rate variability was independently associated with reduced risk of hip fracture among female participants. Purpose: Autonomic nervous system function modulates bone remodeling in rodent osteoporosis models. We tested whether cardiovascular autonomic function is associated with hip fracture risk in humans. Methods: Participants were 1299 subjects from the Cardiovascular Health Study (mean age 72.8 years). Eight heart rate variability (HRV) measures (time and frequency domains, detrended fluctuation analysis variables, and heart rate turbulence) were derived from 24-h Holter monitor scans in sinus rhythm. Median follow-up for incident hip fracture was 14.7 years [IQR 9.1, 20.2]. Cox proportional hazards models were used to calculate hazard ratios (95% confidence intervals, CI). Results: There were 144 hip fractures among 714 women (1.31 [1.06, 1.61] per 100-person years) and 46 among 585 men (0.62 [0.43, 0.90] per 100 person-years). From among HRV variables examined, a one standard deviation (SD) higher variation between normal heart beats over 24 h (the SD of NN intervals [SDNN]) was associated with a multivariable-adjusted lower hip fracture risk (HR = 0.80; 95% CI 0.65–0.99; p = 0.04) in women. The adjusted association between very low frequency power, and hip fracture was borderline statistically significant in women (HR = 0.82; 95% CI, 0.66–1.00; p = 0.06). When the 8 HRV variables were considered conjointly and adjusted for each other's association with hip fracture risk, a 1 SD higher SDNN value was significantly associated with reduced hip fracture risk in women (HR 0.74; 95% CI, 0.50–0.99; p = 0.05). No HRV variables were associated with hip fracture in men. Conclusions: In older women, increased heart rate variation is associated with hip fracture risk. [ABSTRACT FROM AUTHOR]

The extraction of effective signals of microseismic event caused by water inrush in tunnels is an important prerequisite for accurate location of event. In this paper, based on the characteristics of the main frequency components of the tunnel water burst signal, the variational modal method (VMD) with good frequency decomposition ability is adopted to effectively extract the high-frequency components of the water inrush signal. The effective modes are identified by the method of detrend analysis, and the modal decomposition number K is determined. At the same time, the boundary between noise and effective signal is determined. The method proposed in this paper solves the problem that noise in some modal components is mixed with effective signals, and further improves the ability of extracting weak high-frequency signals. Through the analysis of case data, the validity of the research results in this paper is verified, and an effective signal extraction method of tunnel water inrush microseismic event based on VMD-DFA principle is formed. [ABSTRACT FROM AUTHOR]

The main objective of the study was to develop a low-cost, non-invasive diagnostic model for the early prediction of T2DM risk and validation of this model on patients. The model was designed based on the machine learning classification technique using non-linear Heart rate variability (HRV) features. The electrocardiogram of the healthy subjects (n = 35) and T2DM subjects (n = 100) were recorded in the supine position for 15 min, and HRV features were extracted. The significant non-linear HRV features were identified through statistical analysis. It was found that Poincare plot features (SD1 and SD2) can differentiate the T2DM subject data from healthy subject data. Several machine learning classifiers, such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis, Naïve Bayes, and Gaussian Process Classifier (GPC), have classified the data based on the cross-validation approach. A GP classifier was implemented using three kernels, namely radial basis, linear, and polynomial kernel, considering the ability to handle the non-linear data. The classifier performance was evaluated and compared using performance metrics such as accuracy(AC), sensitivity(SN), specificity(SP), precision(PR), F1 score, and area under the receiver operating characteristic curve(AUC). Initially, all non-linear HRV features were selected for classification, but the specificity of the model was the limitation. Thus, only two Poincare plot features were used to design the diagnostic model. Our diagnostic model shows the performance using GPC based linear kernel as AC of 92.59%, SN of 96.07%, SP of 81.81%, PR of 94.23%, F1 score of 0.95, and AUC of 0.89, which are more extensive compared to other classification models. Further, the diagnostic model was deployed on the hardware module. Its performance on unknown/test data was validated on 65 subjects (healthy n = 15 and T2DM n = 50). Considering the desirable performance of the diagnostic model, it can be used as an initial screening test tool for a healthcare practitioner to predict T2DM risk. [ABSTRACT FROM AUTHOR]

The control of voluntary movements is a dual structure consisting of cognitive and physical controls; cognitive control involves attentional resources, unlike physical control. Various voluntary movements can be performed by combining cognitive and physical controls. Body movements depending on attentional resources are performed using cognitive control; these movements fluctuate with white noise and their fluctuations gradually become the one-over-f fluctuation as the dependence on the attentional resources decreases. The handwriting process of characters is a voluntary movement. This study focused on the relationship between the repetitive handwriting process and the attentional resources allocated to it. The attention resources allocated to the handwriting process depend on the difficulty of the handwriting task. Moreover, the difficulty of a handwriting task is determined by the complexity of the shape of the handwritten characters, and the stroke counts is one indicator of this. Therefore, we focused on five Chinese kanji characters with different stroke counts. We investigated fluctuations in the temporal and spatial handwriting elements (twelve handwriting elements in all) defined within the handwriting process of each Chinese kanji character. The detrended fluctuation analysis was applied to the fluctuation analysis of the handwriting elements. In this paper, we discussed the relationship between the handwriting task difficulties and handwriting elements using local scaling indices based on the detrended fluctuation analysis. [ABSTRACT FROM AUTHOR]

Every woman spends her life with a cyclic occurrence of the menstrual cycle of approximately 28 days. During a menstrual cycle, there are many physical, psychological and behavioural changes in women that affect the autonomic activities of the heart. However, health management is an important factor to be considered as it influences the entire quality of women life. Thus, Heart Rate Variability (HRV) analysis is an appropriate tool to examine the physiological effects of the menstrual cycle in young healthy women. So, in this paper, Detrended Fluctuation Analysis method (DFA) is used for analyzing the HRV in the phases of the menstrual cycle. In DFA, there is a limitation of abrupt jumps in detrended profile that leads to the inaccurate detection of properties of HRV. In order to overcome the limitations of abrupt jumps in the DFA method, a novel method is proposed in this paper. The trend in proposed method is created by averaging the integrated HRV time series of each window in its respective time scale. Then, cubic interpolation is done for estimating new data points within each averaged segment. Finally, proposed trend is used to estimate scaling exponent function. The proposed method detects the properties of HRV variations accurately among phases of the menstrual cycle when applied on self-recorded dataset. [ABSTRACT FROM AUTHOR]

The present study is designed to analyse the annual rainfall variability and trend in 30 meteorological stations of the Asir region for the period of 1970–2017 using the Mann-Kendall (MK) test, Modified Mann-Kendall (MMK) test, trend free pre-whitening Mann-Kendall (TFPW MK) test, and the innovative trend analysis (ITA). A comparative study among the trend detection techniques was performed using the correlation coefficient. The future rainfall trend based on the historical rainfall pattern was investigated by using detrended fluctuation analysis (DFA). Results of the MK test showed that 20 stations in the study area observed a negative trend, and out of these, nine stations had significant negative trends at the significance level of 0.01. The findings of the MMK test showed that 23 stations recorded negative trends, and out of these, 18 stations had significant negative trends at the significance level of 0.01. Based on the findings of the TFPW-MK test, 21 stations observed a negative trend, and among these, 10 stations had significant negative trends at the significance of 0.01. ITA detected 25 stations observing a negative trend, and out of these, 18 stations had significant negative trends at the significance level of 0.01. Based on the findings of the tests and their performance, the MMK test appeared as the best performing technique among the MK test family, while ITA appeared as the best trend detection technique among the four techniques because it outperformed (p < 0.01) the others. Results of DFA showed that 23 stations (10 were significant) had recorded declining future rainfall trends based on past trends. The results of the present study would help the planners and policy makers to make accurate and easy decisions on irrigation, climatic, and water resource management in the Asir region of Saudi Arabia. [ABSTRACT FROM AUTHOR]

Long-term memory has been studied for decades and it has long been acknowledged that hydrological and hydro-meteorological time series exhibit this property. Physically, long-term memory is explained by saying that the stronger the long-term memory the more likely a series will 'remember' its previous value, in other words, the longer a time series is likely to persist in the proximity of a certain value. To increase the benefit of the study of long-term memory, we investigate the extent to which this explanation is accurate and descriptive of long-term memory by developing a 'persistence measure' that quantifies the intuitive description of long-term memory. The 'persistence measure' is compared to the scaling exponent (α) which quantifies long-term memory using detrended fluctuation analysis. A total of 17,359 series, including hydrological and hydro-meteorological series, downloaded from online sources and 130 synthetic series are analyzed. The main outcome is that two regimes are found. The first is for series with α ≲ 1.05 where the persistence measure is not sensitive to the scaling exponent. Hence, changes in α does not lead to an increase in the memory strength (i.e. the persistence measure). The second regime is for series with α ≳ 1.05 where a statistically significant positive correlation was found. Hence, for a certain range of values of α the physical explanation of long-term memory holds. Nevertheless, it is proposed that adding additional factors may create a more robust 'persistence measure' that can be easily understood and incorporated in hydrologists' work. [ABSTRACT FROM AUTHOR]

Although the analysis of dependence in financial markets started a century ago, there is still room for new work, both because statistical methods continue to de developed, allowing stronger and more robust analysis, and because more and more data is available. In this context, we propose to make a deep analysis of the Swiss stock market, one of the most important financial centres in the world, studying the main index and also 19 of its 20 components. We use detrended fluctuation analysis, which allows us to analyse the existence of long-term dependence in a given variable. As our objective is to analyse the evolution of that dependence over time, we use a sliding windows approach. The results show that several of the analysed stocks have a behaviour which is not consistent with the absence of dependence, which could be informative for actual and potential investors. [ABSTRACT FROM AUTHOR]

Control of voluntary movements is a dual structure of cognitive and physical controls. Cognitive control involves attentional resources, whereas physical control does not. In this study, we evaluated the dependency on attentional resources while learning to perform physical exercises using the relationship between the attentional resources and fluctuations in body movements. The physical exercise to be learned is the process of writing certain Chinese characters by hand. Detrended fluctuation analysis was applied to the analysis of body movement fluctuations in this study. We herein propose an index that qualitatively evaluates fluctuations locally on a timescale and includes a general scaling index on the detrended fluctuation analysis. We investigate and analyze the fluctuation characteristics in the handwriting process of three Kanji characters using this index. Moreover, we examine the relationship among the six handwriting time elements defined in the handwriting process, the handwriting difficulty of the three Kanji characters, and the timescale. [ABSTRACT FROM AUTHOR]

Detrended fluctuation analysis (DFA) has been recently applied for the investigation of temporal correlations between the earthquake magnitudes before major earthquakes. Here, we employ DFA together with natural time analysis in order to identify precursory phenomena to the M7.1 Ridgecrest earthquake on July 6, 2019. The analysis reveals that a minimum of the variability of the order parameter of seismicity in natural time is observed almost a month before the occurrence of this earthquake. This minimum was observed when DFA indicated the development of the presence of long-range correlations which turned to an almost random behavior before the strong earthquake. Upon starting the study of seismicity in natural time from the minimization of the variability on June 5, 2019, we conclude that criticality has been reached at 22:41 UTC on July 2, 2019, almost 3 days before the earthquake occurrence. The application of an algorithm based on the coherent noise model would have led to a warning 3 min before the occurrence of the M7.1 Ridgecrest earthquake. [ABSTRACT FROM AUTHOR]

We discuss the use of fractal analysis of geophysical big data as a precursory method for the estimation of current seismic hazard. We begin by analyzing GPS time series of land surface displacements recorded in various regions of the planet exhibiting different degrees of seismic activity. We show how the quantitative measure of chaoticity based on a modified detrended fluctuation analysis (DFA) method can be used to distinguish critical and noncritical regimes of evolution of the Earth's crust. Having established the reference values for the measure of chaoticity, we then choose California as the case study region and apply modified DFA to over 1200 time series for determining local areas currently exposed to higher seismic hazard. We comprehensively analyze the results obtained and compare them with those produced by conventional risk assessment methods. We demonstrate consistency of our results and yet discover that the suggested approach allows us to quantify two qualitatively different mechanisms involved in the growth of seismic hazards. We propose a hypothesis which relates these mechanisms to the known mechanisms of earthquake generation that evince a growth of collective behavior in the Earth's crust on the eve of an earthquake. In order to verify the hypothesis, we analyze the same GPS data by an independent statistical method, canonical coherence analysis (CCA), which permits us to quantify the degree of synchronized behavior of constituents of the Earth's crust. The CCA method confirms our previously obtained estimates, thus supporting fractal analysis of GPS time series as a suitable precursory method for current seismic hazard assessment. [ABSTRACT FROM AUTHOR]

This study tests weak form efficiency in frontier markets. Mutual information and detrended fluctuation analyses are performed to assess global correlation and long range dependence in the stock markets of twenty three countries. The results indicate that Slovenia is the only case where there is evidence compatible with weak form efficiency. The relatively less inefficient markets are mainly located in Europe and America, and the relatively more inefficient mainly in the Middle East. This information is useful for investors, but also for the assessed countries' regulators as they indicate that relevant impediments are preventing the exploitation of potential profitable opportunities. [ABSTRACT FROM AUTHOR]

The aim of this study is to assess the impact of aging process in gait variability. Stride interval variability is estimated by using two approaches: (1) a non-linear fractal analysis (detrended fluctuation analysis) which evaluates the presence of long-range correlations in stride interval time series; and (2) a statistical dispersion measure (coefficient of variation) to quantify the magnitude of the stride interval fluctuations. Two groups of physically independent older adults, with different walking ability, have participated in the gait trials performed in an elderly care home. The estimated stride interval variability of the elders is compared to each other and to the variability coming from a young adult group used as control. To accomplish this, an infrastructure which uses wearables to acquire inertial data from the trunk of each participant is provided. Stride interval time series are made up of the estimated heel-strike events from previous inertial data. In addition, a service segments straight paths within the gait trials, discarding turns. The stride intervals from the segmented straight paths are stitched together to produce the long time series required to analyze gait variability. Despite the study has not provided conclusive results from an individual perspective, finding older adults who have less stride interval variability than younger ones. The inter-class analysis conducted has shown interesting findings about the relation between the subjective characterization of gait, aging and stride interval variability estimated through the two proposed approaches. [ABSTRACT FROM AUTHOR]

We study for the first time the correlation properties and the multifractality of downward longwave radiation (DLR) temporal fluctuations, using the multi-fractal detrended fluctuation analysis. Our DLR data are taken from 40 stations of the Baseline Surface Radiation Network and span more than 20 years on temporal resolutions of a few minutes. In almost all stations, two different scaling regions are defined through the emergence of a crossover at around 10 days, similarly to earlier studies of other meteorological quantities. The crossover marks the transition from a persistent, non-stationary signal on weather scales to a more weakly persistent (and sometimes anti-persistent) behavior extending in the macroweather region between 2 months and at least several years. We observe typical multifractal behavior for DLR through the investigation of the generalized Hurst exponents and singularity spectra. Generally, the multifractality strength is larger for the weather regime compared to the macroweather. We find evidence that the shape of the probability density function of the DLR anomalies contributes to the multifractality strength for macroweather. The three Western Equatorial Pacific stations exhibit markedly different characteristics, with fluctuation functions closely following 1/f noise between the scales of a few hours up to 2 months. These stations appear to be strongly affected by the ENSO. Also, their timeseries are barely long enough so that the ENSO shows as a semi-periodic signal on the large scales of their long-term memory. Their multifractality is not clearly defined and its strength is much smaller than that of the other stations. [ABSTRACT FROM AUTHOR]

Activities of daily living require maintaining upright posture within a variety of environmental constraints. A healthy postural control system can adapt to different environmental constraints. Afferent sensory information is used to determine where the body is in relation to the gravitational vertical and efferent motor commands make corrections with the goal of keeping the center of mass within the base of support. The purpose of this research was to understand how vision, direction of translation, and the temporal correlation of the support surface stimuli affected the persistence characteristics of postural dynamics on short and long time scales. Ten healthy young adults performed a standing task with either eyes open or closed, oriented anteriorly or mediolaterally while the support surface underwent structured translations based on different levels of temporal correlation—white noise (no correlation), pink noise (moderate correlation), and red noise and sinusoidal movements (strong correlations). Center of pressure velocity was analyzed using fractal analysis to determine the dynamics of postural control. On the short time scale, persistence was shown to be stronger with eyes closed, in the mediolateral direction, and when the structure of translation contained stronger temporal correlation. On the long time scale, anti-persistence was stronger with eyes closed, in the mediolateral direction, and for all structures of movement except red noise. This study provides deeper insight into the flexibility existing in human movement responses to structured environmental stimuli through the fractal analysis of movement variability. [ABSTRACT FROM AUTHOR]

The sensorimotor system prefers sway velocity information when maintaining upright posture. Sway velocity has a unique characteristic of being persistent on a short time-scale and anti-persistent on a longer time-scale. The time where the transition from persistence to anti-persistence occurs provides information about how sway velocity is controlled. It is, however, not clear what factors affect shifts in this transition point. This research investigated postural responses to support surface movements of different temporal correlations and movement velocities. Participants stood on a force platform that was translated according to three different levels of temporal correlation. White noise had no correlation, pink noise had moderate correlation, and sine wave movements had very strong correlation. Each correlation structure was analyzed at five different average movement velocities (0.5, 1.0, 2.0, 3.0, and 4.0 cm·s−1), as well as one trial of quiet stance. Center of pressure velocity was analyzed using fractal analysis to determine the transition from persistent to anti-persistent behavior, as well as the strength of persistence. As movement velocity increased, the time to transition became longer for the sine wave and shorter for the white and pink noise movements. Likewise, during the persistent time-scale, the sine wave resulted in the strongest correlation, while white and pink noise had weaker correlations. At the highest three movement velocities, the strength of persistence was lower for the white noise compared to pink noise movements. These results demonstrate that the predictability and velocity of support surface oscillations affect the time-scale threshold between persistent and anti-persistent postural responses. Consequently, whether a feedforward or feedback control is utilized for appropriate postural responses may also be determined by the predictability and velocity of environmental stimuli. The study provides new insight into flexibility and adaptability in postural control. This information has implications for the design of rehabilitative protocols in neuromuscular control. [ABSTRACT FROM AUTHOR]

We present the results of applying detrended fluctuation analysis (DFA) to study correlations in mountain stream temperatures over a 9-year period for impacted and unimpacted conditions. Permutation entropy and complexity are also computed for stream temperature time series at various locations characterized by varying degrees of human impact, and a graphical representation of the “complexity-entropy causality plane” is used to examine the degree of disturbance caused by human activities. Urban development such as canopy removal and urban infrastructure can lead to a higher degree of anti-persistent correlations indicated by lower values of DFA exponents. This effect is more pronounced downstream due to cumulative urbanization effects with downstream distance. Seasonal variations also influence stream temperature dynamics: less anti-persistent behavior is observed in winter months, and stronger anti-persistent correlations during summer. The position in the complexity-entropy plane, reflecting distribution of short term temperature patterns, is also affected, where most sites experience a shift towards higher entropy values after restoration, and only for one site entropy is reduced. Our results indicate the important role urbanization plays on stream evolution greatly influenced by human activities. This information may provide valuable insights for watershed management and water resources management. [ABSTRACT FROM AUTHOR]

The daily temperatures from observational data, NCEP-2 and CFSR reanalysis data all exhibit long-range correlation (LRC) characteristics, which provides a test bed for assessing the reliability of reanalysis data. In this study, the quality of the NCEP-2 and CFSR data in China are evaluated on the basis of the LRC characteristics of daily temperatures, including daily average temperature (DAT), daily maximum temperature (DMAT), daily minimum temperature (DMIT), and diurnal temperature range (DTR). Compared with the observations, the quality of NCEP-2 daily temperature is relatively good in central and eastern Northwest China, and most of central and eastern China, especially for NCEP-2 DMAT. However, the NCEP-2 reanalysis data as well as CFSR has a significant difference with the LRC of the observations in most of Sichuan, Qinghai-Tibet Plateau and some areas of southwestern Xinjiang at a significance level of Alpha = 0.05. In general, the LRC characteristics of NCEP-2 daily temperature perform better than that of CFSR data. As far as DAT is concerned, CFSR perform worse in central and eastern Northwest China, and better than NCEP-2 only in South China and eastern Jiangnan. The quality of the CFSR DMAT is worse than that of NCEP-2 in central and eastern Northwest China, western Inner Mongolia, and eastern China. The quality of NCEP-2 DMIT is better than CFSR in central and eastern Northwest China, most of Inner Mongolia, and is worse than it in most of South China and eastern Jiangnan. The reliability of the CFSR DTR is very low in most of China. [ABSTRACT FROM AUTHOR]

Factor portfolios derived from phenomena identified in the cross-section of stock returns have become vital parts of modern investment products and financial models. Even though much has been learned about the properties of these portfolios in recent years, one issue still remains unaddressed. Are factor returns long-range dependent (LRD)? We seek to answer this important research question because if factor returns were LRD, optimal portfolio decisions and traditional asset pricing methods/tests based on these factors would be severely biased and the validity of a large strand of prior research would be compromised. Specifically, using Hurst exponent approaches within rescaled range and detrended fluctuation frameworks, we analyse the presence of LRD in the returns of factor portfolios formed based on size, book-to-market, momentum and beta characteristics. For the periods from 1931 to 2014 (US market) and 1990 to 2014 (20 international markets) and supported by several robustness checks, we find no systematic evidence of persistence or anti-persistence in the factor returns. This implies that the factor use can be considered unproblematic in both asset management and asset pricing. [ABSTRACT FROM AUTHOR]

Infantile spasms syndrome is an epileptic encephalopathy in which prompt diagnosis and treatment initiation are critical to therapeutic response. Diagnosis of the disease heavily depends on the identification of characteristic electroencephalographic (EEG) patterns, including hypsarrhythmia. However, visual assessment of the presence and characteristics of hypsarrhythmia is challenging because multiple variants of the pattern exist, leading to poor inter-rater reliability. We investigated whether a quantitative measurement of the control of neural synchrony in the EEGs of infantile spasms patients could be used to reliably distinguish the presence of hypsarrhythmia and indicate successful treatment outcomes. We used autocorrelation and Detrended Fluctuation Analysis (DFA) to measure the strength of long-range temporal correlations in 21 infantile spasms patients before and after treatment and 21 control subjects. The strength of long-range temporal correlations was significantly lower in patients with hypsarrhythmia than control patients, indicating decreased control of neural synchrony. There was no difference between patients without hypsarrhythmia and control patients. Further, the presence of hypsarrhythmia could be classified based on the DFA exponent and intercept with 92% accuracy using a support vector machine. Successful treatment was marked by a larger increase in the DFA exponent compared to those in which spasms persisted. These results suggest that the strength of long-range temporal correlations is a marker of pathological cortical activity that correlates with treatment response. Combined with current clinical measures, this quantitative tool has the potential to aid objective identification of hypsarrhythmia and assessment of treatment efficacy to inform clinical decision-making. [ABSTRACT FROM AUTHOR]

The performance and measurement accuracy of global navigation satellite system (GNSS) receivers is greatly affected by ionospheric scintillations. Rapid amplitude and phase variations in the received GPS signal, known as ionospheric scintillation, affects the tracking of signals by GNSS receivers. Hence, there is a need to investigate the monitoring of various activities of the ionosphere and to develop a novel approach for mitigation of ionospheric scintillation effects. A method based on Local Mean Decomposition (LMD)-Detrended Fluctuation Analysis (DFA) has been proposed. The GNSS data recorded at Koneru Lakshmaiah (K L) University, Guntur, India were considered for analysis. The carrier to noise ratio (C/N) of GNSS satellite vehicles were decomposed into several product functions (PF) using LMD to extract the intrinsic features in the signal. Scintillation noise was removed by the DFA algorithm by selecting a suitable threshold. It was observed that the performance of the proposed LMD-DFA was better than that of empirical mode decomposition (EMD)-DFA. [ABSTRACT FROM AUTHOR]

When two eyes are simultaneously stimulated by two inconsistent images, the observer's perception switches between the two images every few seconds such that only one image is perceived at a time. This phenomenon is named binocular rivalry (BR). However, sometimes the perceived image is a piecemeal mixed of two stimuli known as piecemeal perceptions. In this study, a BR task was designed in which orthogonal gratings are presented to the two eyes. The subjects were trained to report 3 states: dominant perceptions (two state matching to perceived grating orientation) and the piecemeal perceptions (third state). We explored the scale-freeness of the BR percept durations considering the two dominant monocular states as well as the piecemeal transition state using detrended fluctuation analysis. Our results reproduced the previous finding of memory in the perceptual switches between the monocular perception states. Moreover, we showed that such memory also exists in the transitory periods of dichoptic piecemeal perceptions. These results support our hypothesis that the pool of unstable piecemeal perceptions could be regarded as separate multiple low-depth basin in the perceptual state landscape. Likewise, the transitions from these piecemeal state basins and stable monocular state basins are faced with resistance. Therefore there is inertia and memory (i.e. positive serial correlation) for the piecemeal dichoptic perception states as well as the monocular states. [ABSTRACT FROM AUTHOR]

Sensory feedback below the sole of the foot using sub-threshold mechanical noise significantly reduced postural sway in patients with diabetes and stroke. However, the effects of tactile parameters on walking are still elusive. Specifically, the effects of such parameters on human gait variability need to be studied because of possible rehabilitation outcomes in terms of bringing improvement in temporal and spatial gait parameters. The purpose of this study was to investigate whether different frequency and amplitude combinations of vibro-tactile stimulation of feet would affect stride-to-stride variability in healthy young adults. Ten healthy subjects walked on a treadmill at self-selected pace while wearing customized insoles fitted with tactors that vibrated at selected frequencies and amplitudes. The results show that the frequency manipulations of tactile stimulation altered the long-range correlations (LRCs) in stride length while amplitude manipulations affected the LRCs in stride interval without having any effect on the amount of gait variability. Our findings suggest that independent neural mechanisms may be responsible for coordinating LRCs of gait parameters in the spatial and temporal domains. [ABSTRACT FROM AUTHOR]

This study analyzed the postural control of older adults on inclined surfaces, and was conducted in 17 elderly adults and 18 young adults of both genders. Ground reaction forces and moments were collected using two AMTI force platforms, one of which was in a horizontal position (HOR), while the other was inclined 14° in relation to the horizontal plane. Each participant executed three 70 s-trials of bipedal standing with their eyes open and eyes closed in three inclination conditions: the HOR, the inclined position at ankle dorsi-flexion (UP), and the inclined position at ankle plantar-flexion (DOWN). Spectral analysis, global (mean velocity-Velm, ellipse area-Area and F80), and structural stabilometric descriptors (sway density curve-SDC, detrended fluctuation analysis-DFA, sample entropy-SEn) were employed to assess the center of pressure sway. Velm and F80 were greater for the elderly, whereas SDC, DFA, and SEn were smaller for this group. Global, SDC and DFA variables were sensitive to visual deprivation, however the relative difference from the EO to EC condition was higher in young than in elderly. The DOWN condition was more stable than the UP condition for both young and older adults. With regard to the UP condition, the challenge observed is essentially associated with the corresponding biomechanical constraints. In conclusion, the elderly showed significant differences compared to the young, but age per se may not necessarily result in compromised postural control. [ABSTRACT FROM AUTHOR]

In order to explore long-term evolution rule and future trend of runoff time series, and exactly detect its tendency and long-range correlation characteristics, runoff data covering 1952-2012 from 3 stations across the upper Fenhe River basin were analyzed. The moving average method, Empirical Mode Decomposition (EMD) method and Mann-Kendall (M-K) trend test method were simultaneously applied to analyze the trend characteristics firstly. Then Rescaled Range analysis ( R/ S) and Detrended Fluctuation Analysis (DFA) methods were employed to research the long-range correlation characteristics and length of non-periodic cycle of hydrological time series, they can systematically detect and overcome non-stationarity at all time scales. Finally, predict the future trend by combining the trend characteristics with the long-range correlation characteristics and length of non-periodic cycle. The results illustrate the annual runoff series is non-linear, non-normal time series, and have 10 years non-periodic cycle length and noticeable descending trend. This descending trend will continue in a period time of future. [ABSTRACT FROM AUTHOR]

A hybrid statistical downscaling method based on the classification of rainfall patterns is presented which is capable of overcoming the poor representation of extreme events. The large-scale datasets, which are obtained from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data and the global circulation models (GCMs) outputs, and the local daily rainfall data are analyzed to assess the impacts of climate change on rainfall. The proposed method is composed of two steps. The first step is the classification of daily rainfall patterns. The detrended fluctuation analysis (DFA) is introduced to define the extreme rainfall. Two classification models, extreme rainfall and wet rainfall, are developed to describe the relationship between large-scale weather factors and rainfall patterns using support vector machine (SVM). These two models are able to identify the three rainfall patterns (the extreme, the normal and the dry rainfall) of the daily weather factors. The second step is the estimation of daily rainfall. The improved self-organizing linear output map (ISOLO) is adopted to estimate the rainfall for the aforementioned three different rainfall patterns. The future rainfall changes are calculated for the periods 2046-2065 and 2081-2100 under the A2 and B1 scenarios. An application to Taiwan has shown that the proposed method provides reliable and accurate rainfall-pattern classification. In addition, the improvement of the estimation of daily rainfall is significant, especially for the extreme rainfall. In conclusion, the proposed method is effective to overcome the poor representation of extreme events and the impacts of climate change on rainfall are analyzed. [ABSTRACT FROM AUTHOR]

The fractal characteristics of the ultra-low-frequency (ULF) magnetic field variations recorded prior to the Tohoku earthquake (EQ) with M = 9 which happened on 11 March 2011 are studied in this article with the use of detrended fluctuation analysis and Higuchi fractal dimension algorithm. In the specific study, we use for our calculations only nighttime (LT = 3 a.m. ± 2 h) data because of their lowest contamination by industrial noise. A key aspect of our analysis is the investigation about any possible correlation of the ULF magnetic field variations or their calculated fractal characteristics with geomagnetic indices. Different preprocessing approaches are examined aiming at the minimization of any possible influences from global phenomena in the fractal analysis results, while in the same time retaining the scale-invariant character of ULF magnetic field variations after preprocessing. The obtained fractal analysis results imply locally driven change in the fractal characteristics of the ULF data prior to the Tohoku EQ, which is compatible with the change that has been reported prior to other large EQs. [ABSTRACT FROM AUTHOR]

Healthy human heart rate fluctuates overtime showing long-range fractal correlations. In contrast, various cardiac diseases and normal aging show the breakdown of fractal complexity. Recently, it was shown that human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) intrinsically exhibit fractal behavior as in humans. Here, we investigated the fractal complexity of hiPSC-derived long QT-cardiomyocytes (LQT-CMs). We recorded extracellular field potentials from hiPSC-CMs at baseline and under the effect of various compounds including β-blocker bisoprolol, ML277, a specific and potent I current activator, as well as JNJ303, a specific I blocker. From the peak-to-peak-intervals, we determined the long-range fractal correlations by using detrended fluctuation analysis. Electrophysiologically, the baseline corrected field potential durations (cFPDs) were more prolonged in LQT-CMs than in wildtype (WT)-CMs. Bisoprolol did not have significant effects to the cFPD in any CMs. ML277 shortened cFPD in a dose-dependent fashion by 11 % and 5-11 % in WT- and LQT-CMs, respectively. JNJ303 prolonged cFPD in a dose-dependent fashion by 22 % and 7-13 % in WT- and LQT-CMs, respectively. At baseline, all CMs showed fractal correlations as determined by short-term scaling exponent α. However, in all CMs, the α was increased when pharmacological compounds were applied indicating of breakdown of fractal complexity. These findings suggest that the intrinsic mechanisms contributing to the fractal complexity are not altered in LQT-CMs. The modulation of I channel and β1-adrenoreceptors by pharmacological compounds may affect the fractal complexity of the hiPSC-CMs. [ABSTRACT FROM AUTHOR]

The stability of current methods of complexity measurement are generally Inefficient. In this study, multifractal spectra (MFS) analysis, which depends on empirical mode decomposition detrended fluctuation analysis (EMD-DFA), was used to measure the complexity of the monthly precipitation series from 1964 to 2013 (50 years) of 11 districts in Harbin, Heilongjiang Province, China. By comparing the anti-noise capability of MFS-EMD-DFA with that of conventional complexity measurement approaches, such as sample entropy, Lempel-Ziv complexity, and approx mate entropy, it was established that MFS-EMD-DFA has greater robustness in anti-noise jamming, and thus it could be applied more widely. The precipitation series complexity strength map of the 11 regions was drawn using a geographical information system. This study analyzed the correlation between precipitation and some meteorological factors and then ranked their strengths. The results showed that many meteorological factors have strong connections with the regional precipitation series in the study area. This study provided a solid foundation for further extraction of hydrological information in Harbin and proposed a new method for complexity analysis. The novel MFS-EMD-DFA approach could also be applied to the analysis of multifractal characteristics as well as complexity measurement in various other disciplines. [ABSTRACT FROM AUTHOR]

In the paper, the multifractal detrended fluctuation analysis is used to investigate the multifractal characteristics of gold grades series in the Dayingezhuang gold deposit, Jiaodong province, China. Results obtained from the multifractal parameters and multifractal spectra show that the gold grades series exhibit multifractal scaling and different long-range correlations. The asymmetry of multifractal spectrum shows that the range of the right-side branches of the multifractal spectrum is larger than that of the left-side branches for the gold grades series of intensely mineralized drifts, but the gold grades series of the barely mineralized zones is on the contrary, which indicates that the shape of of multifractal spectrum may provide directions in the study of ore-forming potential. Moreover, we analyze the sources of multifractality quantifying the contributions of two factors, the long-range correlations and the broad fat-tail distributions. We find that the multifractality structure of gold grades series is due to both long-range correlation and the fat-tail probability density function, but the former is greater attributed to the latter. [ABSTRACT FROM AUTHOR]

In this paper, we use a combination of numerical simulations, time series analysis, and complexity measures to investigate the dynamics of switched systems with noise, which are often used as models of human balance control during quiet standing. We link the results with complexity measures found in experimental data of human sway motion during quiet standing. The control model ensuring balance, which we use, is based on an act-and-wait control concept, that is, a human controller is switched on when a certain sway angle is reached. Otherwise, there is no active control present. Given a time series data, we determine how does it look a typical pattern of control strategy in our model system. We detect the switched nonlinearity in the system using a frequency analysis method in the absence of noise. We also analyse the effect of time delay on the existence of limit cycles in the system in the absence of noise. We perform the entropy and detrended fluctuation analyses in view of linking the switchings (and the dead zone) with the occurrences of complexity in the model system in the presence of noise. Finally, we perform the entropy and detrended fluctuation analyses on experimental data and link the results with numerical findings in our model example. [ABSTRACT FROM AUTHOR]

In order to improve the visualization simulation effect of water wave, we use the images of actual water wave as a feedback to correct the control parameters of Shallow Water (SW) equations in this paper. First, we employ a kind of simplified numerical method to resolve the SW equations and create an initial water wave animation. The initial control parameters of SW equations can be set arbitrarily. Second we cut some water wave images from the artificial animation above to build an image dataset of Simulated Water Wave (SWW). Third, we capture the Actual Water Wave (AWW) images by cameras, which are fixed in the selected locations of a moving boat, to build another image dataset. After that, we make a correlation analysis of image texture between the artificial image dataset of SWW and the actual image dataset of AWW to compare their similarity. In this phase, some image quality metrics of Tamura's texture, together with the mathematic tools of Mojette transform, Gabor wavelet and Detrended Fluctuation Analysis (DFA) technique are utilized to accomplish the static and dynamic texture analysis tasks. Finally, we use the results of correlation analysis above as a feedback to give a guidance to tune the control parameters of SW equations and regenerate the water wave animation with better visualization effects. To enhance the fidelity of SWW images, we also use Gabor wavelet and the criterion of minimized distance to estimate the environment illumination direction of AWW if the texture definition is good enough. By this means, we can set proper light source parameters to the visualization animation of SWW. Extensive experiment results have shown us that the visualization simulation effect can be improved effectively by the application of our texture feedback based techniques. [ABSTRACT FROM AUTHOR]

Seismic attributes have been widely used in oil and gas exploration and development. However, owing to the complexity of seismic wave propagation in subsurface media, the limitations of the seismic data acquisition system, and noise interference, seismic attributes for seismic data interpretation have uncertainties. Especially, the antinoise ability of seismic attributes directly affects the reliability of seismic interpretations. Gray system theory is used in time series to minimize data randomness and increase data regularity. Detrended fluctuation analysis (DFA) can effectively reduce extrinsic data tendencies. In this study, by combining gray system theory and DFA, we propose a new method called gray detrended fluctuation analysis (GDFA) for calculating the fractal scaling exponent. We consider nonlinear time series generated by the Weierstrass function and add random noise to actual seismic data. Moreover, we discuss the antinoise ability of the fractal scaling exponent based on GDFA. The results suggest that the fractal scaling exponent calculated using the proposed method has good antinoise ability. We apply the proposed method to 3D poststack migration seismic data from southern China and compare fractal scaling exponents calculated using DFA and GDFA. The results suggest that the use of the GDFA-calculated fractal scaling exponent as a seismic attribute can match the known distribution of sedimentary facies. [ABSTRACT FROM AUTHOR]

Correlation dimension (CD) is used for analysing the chaotic behaviour of the nonlinear heart rate variability (HRV) time series. In CD, the autocorrelation function is used to calculate the time delay. However, it does not provide optimum values of time delays, which leads to an inaccurate estimation of the HRV between phases of the menstrual cycle. Thus, an adaptive CD method is presented here to calculate the optimum value of the time delay based upon the information content in the HRV signal. In the proposed method, the first step is to divide the HRV signal into overlapping windows. Afterwards, the time delay is calculated for each window based on the features of the signal. This procedure of finding the optimum time delay for each window is known as adaptive autocorrelation. Then, the CD for each window is calculated using optimum time delays. Finally, adaptive CD is calculated by averaging the CD of all windows. The proposed method is applied on two data sets: (i) the standard Physionet dataset and (ii) the dataset acquired using BIOPACMP150. The results show that the proposed method can accurately differentiate between normal and diseased subjects. Further, the results prove that the proposed method is more accurate in detecting HRV variations during the menstrual cycles of 74 young women in lying and standing postures. Three statistical parameters are used to find the effectiveness of adaptive autocorrelation in calculating time delays. The comparative analysis validates the superiority of the proposed method over detrended fluctuation analyses and conventional CD. [ABSTRACT FROM AUTHOR]

Acute hypoxia activates several autonomic mechanisms, mainly in cardiovascular system and respiratory system. The influence of acute hypoxia on linear and nonlinear heart rate variability (HRV) has been studied, but the parameters in the process of hypoxia are still unclear. Although the changes of HRV in frequency domain are related to autonomic responses, how nonlinear dynamics change with the decrease of ambient atmospheric pressure is unknown either. Eight healthy male subjects were exposed to simulated altitude from sea level to 3600 m in 10 min. HRV parameters in frequency domain were analyzed by wavelet packet transform (Daubechies 4, 4 level) followed by Hilbert transform to assess the spectral power of modified low frequency (0.0625-0.1875 Hz, LF), modified high frequency (0.1875-0.4375 Hz, HF), and the LF/HF ratio in every 1 min. Nonlinear parameters were also quantified by sample entropy (SampEn) and short term fractal correlation exponent (α1) in the process. Hypoxia was associated with the depression of both LF and HF component. They were significantly lower than that at sea level at 3600 m and 2880 m respectively (both p < 0.05). The LF/HF ratio was acutely increased at 3600 m (p < 0.05). SampEn was significantly declined at 2880 m (p < 0.05). Although the value of α1 was close to 1, it changed not significantly in the whole process. These results indicated hypoxia gradually attenuated both spectral HRV parameters and SampEn. The balance of sympathovagal shifted towards sympathetic dominance at a certain altitude. Monitoring linear and nonlinear HRV parameters continuously in the process of hypoxia would be an effective way to evaluate the different regulatory mechanisms of autonomic nervous system. [ABSTRACT FROM AUTHOR]

We analyzed the fractal and multifractal properties of the earthquake time series occurred around the Enguri dam in West Georgia by applying the methods of detrended fluctuation analysis and multifractal detrended fluctuation analysis. We examined the interevent time series in two periods: (1) 1960-1980, in which the investigated area was characterized by the natural seismicity; and (2) 1981-2012, in which the quasi-periodic change of the reservoir water level affected the earthquake generation. Our findings show that the water level variation may influence the fractal properties of earthquake temporal distribution in the local area around the Enguri dam. In particular, it is observed that the time distribution features of seismicity occurred in the second period are more persistent than the natural seismicity occurred in the first period. Furthermore, the seismic process of the second period shows a lower multifractal degree than that of the first period, indicating that the influence of quasi-periodic fluctuation of water level features the seismicity as more regular compared to the natural seismicity. [ABSTRACT FROM AUTHOR]

Purpose: Endurance exercise is associated with high cardiac vagal tone, but how the cardiac autonomic control correlates with shorter anaerobic performances is unknown. Therefore, the aim of this study was to evaluate how autonomic modulations of heart rate (HR) variability (V) correlate with performances of short- (<1 min) and very short (<30 s) duration in elite athletes. Method: Thirteen male swimmers, national-level crawl specialists in short (100-m) and very short (50-m) distances, were enrolled. HR was recorded during 15-min supine rest: (1) in the morning after wake up, (2) in the afternoon before sprint-oriented training sessions, (3) few minutes after training (first recovery phase after swimming cooldown). Heart rate variability (HRV) vagal and sympatho/vagal indices were calculated in time, frequency and complexity domains. Correlations of best seasonal times on 100- or 50-m distances with HRV indices and the velocity at blood lactate accumulation onset ( V) were calculated. Results and conclusion: Vagal indices were highest in the morning where they positively correlated with very short-distance times (higher the index, worse is the 50-m performance). Sympatho/vagal indices were highest after training where they negatively correlated with short-distance times (higher the index, better is the 100-m performance). V did not correlate with the performances. Therefore, autonomic HRV indices and not V predict short and very short, most anaerobic, performances. Results also suggest that a strong cardiac vagal control has no effect on short performances and is even detrimental to very short performances, and that the capacity to powerfully increase the sympathetic tone during exercise may improve short, but not very short performances. [ABSTRACT FROM AUTHOR]