Search

In this paper, we thoroughly analyze the detection of sleep apnea events in the context of Obstructive Sleep Apnea (OSA), which is considered a public health problem because of its high prevalence and serious health implications. We especially evaluate patients who do not always show desaturations during apneic episodes (non-desaturating patients). For this purpose, we use a database (HuGCDN2014-OXI) that includes desaturating and non-desaturating patients, and we use the widely used Physionet Apnea Dataset for a meaningful comparison with prior work. Our system combines features extracted from the Heart-Rate Variability (HRV) and SpO2, and it explores their potential to characterize desaturating and non-desaturating events. The HRV-based features include spectral, cepstral, and nonlinear information (Detrended Fluctuation Analysis (DFA) and Recurrence Quantification Analysis (RQA)). SpO2-based features include temporal (variance) and spectral information. The features feed a Linear Discriminant Analysis (LDA) classifier. The goal is to evaluate the effect of using these features either individually or in combination, especially in non-desaturating patients. The main results for the detection of apneic events are: (a) Physionet success rate of 96.19%, sensitivity of 95.74% and specificity of 95.25% (Area Under Curve (AUC): 0.99); (b) HuGCDN2014-OXI of 87.32%, 83.81% and 88.55% (AUC: 0.934), respectively. The best results for the global diagnosis of OSA patients (HuGCDN2014-OXI) are: success rate of 95.74%, sensitivity of 100%, and specificity of 89.47%. We conclude that combining both features is the most accurate option, especially when there are non-desaturating patterns among the recordings under study. [ABSTRACT FROM AUTHOR]

Background: Heart–brain synchronization is the integration of mind, body, and spirit. It occurs when the electrical activity of the heart and brain is synchronized. In recent years, there has been mounting curiosity to investigate the effects of meditation on heart–brain synchronization with respect to mental and emotional health and well-being. The current investigation aims to explore the rhythmic synchronicity between the brain and the heart during heartfulness meditation (HM) practice. Materials and Methods: The study was performed on 45 healthy volunteers who were categorized into three equal groups: long-term meditators (LTMs), short-term meditators (STMs), and nonmeditators (NMs). The electroencephalogram (EEG) signals were recorded to measure the prefrontal activity, and electrocardiogram (ECG) signals were recorded to measure the cardiac activity. The data were recorded in four states: baseline, meditation, transmission, and posttransmission. The detrended fluctuation analysis (DFA) method was used for the analysis of EEG and ECG signals. Results: The result indicates that DFA values of EEG and ECG declined during meditation and transmission states as compared to pre- and postmeditation states. Significant results were obtained for the LTM group in all the states. A positive correlation was also observed between DFA of the heart and brain for the LTM group and no significant correlations were observed for the STM and NM groups. Conclusion: The shreds of evidence suggest that heart–brain synchronization facilitates mental and emotional stability. HM practice has the potential to regulate the fluctuation of the mind. Regular meditation practice may result in physiological synchrony between cardiac and neural behavior, which can be considered a quality index for meditation practice. [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]

Vehicle driving cycles have complex characteristics, but there are few publicly reported methods for their quantitative characterization. This paper innovatively investigates their multifractal characteristics using the fractal theory to characterize their complex properties, laying the foundation for applications such as vehicle driving cycle feature identification, vehicle energy management strategies (EMS), and so on. To explore the scale-invariance of the vehicle driving cycles, the four vehicle driving cycles were analyzed using the Multifractal Detrended Fluctuation Analysis (MF-DFA) method, three of which are standard vehicle test cycles: the New European Driving Cycle (NEDC), the World-wide harmonized Light-duty Test Cycle (WLTC) and the China Light-duty Vehicle Test Cycle for Passenger Car (CLTC-P), and the other is the Urban Road Real Driving Cycle (URRDC), which was obtained by analyzing and processing vehicle driving data collected in actual urban driving conditions. The fluctuation functions, the generalized Hurst exponents, the mass exponent spectra, the multifractal singularity spectra, and the multifractal characteristic parameters were calculated to verify the multifractal characteristics, and to quantify the fluctuation singularities of different driving cycles as the time series. The results show that the fluctuations of all four driving cycles have long-range anticorrelations and exhibit significant multifractal characteristics. The results can provide a basis for the analysis of the complexity of the vehicle driving cycles. [ABSTRACT FROM AUTHOR]

Abstract Background Pulmonary vein isolation (PVI) is a cornerstone therapy for paroxysmal atrial fibrillation (PAF). The variations in nonlinear heart rate variability (HRV) between patients with and without recurrences remain unclear. We aimed to characterize the nonlinear HRV before and after PVI in patients with and without recurrence. Methods Twenty‐five drug‐refractory PAF patients (56.0 ± 9.1 years old, 20 males) who received PVI were enrolled. Holter electrocardiography were performed before, 1–3, and 6–12 months after PVI. After 8.2 ± 2.5 months of follow‐ups after PVI, patients were divided into two groups: the recurrence (n = 8) and non‐recurrence (n = 17) groups. Linear and nonlinear HRV variables were analyzed, including the Poincaré Plot analysis and the Detrended Fluctuation Analysis (DFA). Results The non‐recurrence group, but not the recurrence group, had decreased high‐frequency component (HF), the root mean square of successive RR interval differences (RMSSD), and the Poincaré Plot index SD1 1–3 months after PVI and increased DFAslope2 6–12 months after PVI. The non‐recurrence group's LF/HF ratio and DFAslope1 decreased significantly 1–3 and 6–12 months after PVI, respectively, whereas there was no significant change in the recurrence group after PVI. Conclusions Significantly reduced vagal tone 1–3 months after PVI, increased long‐term fractal complexity 6–12 months after PVI, and decreased sympathetic tone as well as short‐term fractal complexity 1–3 and 6–12 months after PVI led to a better AF‐free survival after PVI. These findings suggest that neuromodulation and heart rate dynamics play crucial roles in AF recurrence following PVI.

Introduction: Aging is a physiological process characterized by progressive changes in all organ systems. In the last few decades, the elderly population has been growing, so the scientific community is focusing on the investigation of the aging process, all in order to improve the quality of life in elderly. One of the biggest challenges in studying the impact of the aging on the human body represents the monitoring of the changes that inevitably occur in arterial blood vessels. Therefore, the medical community has invested a great deal of effort in studying and discovering new methods and tools that could be used to monitor the changes in arterial blood vessels caused by the aging process. The goal of our research was to develop a new diagnostic method using a photoplethysmographic sensor and to examine the impact of the aging process on the cardiovascular system in adults. Long-term recorded arterial blood flow waveforms were analyzed using detrended fluctuation analysis.Materials and Methods: The study included 117 respondents, aged 20–70 years. The waveform of the arterial blood flow was recorded for 5 min, with an optical sensor placed above the left common carotid artery, simultaneously with a single-channel ECG. For each cardiac cycle, the blood flow amplitude was determined, and a new time series was formed, which was analyzed non-linearly (DFA method). The values of the scalar coefficients α1 and α2, particularly their ratio (α1/α2) were obtained, which were then monitored in relation to the age of the subjects.Result: The values of the scalar ratio (α1/α2) were significantly different between the subjects older and younger than 50 years. The value of the α1/α2 decreased exponentially with the aging. In the population of middle-aged adults, this ratio had a value around 1, in young adults the value was exclusively higher than 1 and in older adults the value was exclusively lower than 1.Conclusion: The results of this study indicated that the aging led to a decrease in the α1/α2 in the population of healthy subjects. With this non-invasive method, changes in the cardiovascular system due to aging can be detected and monitored.

Abstract Introduction Detrended fluctuation analysis (DFA) is a well‐established method to evaluate scaling indices of time series, which categorize the dynamics of complex systems. In the literature, DFA has been used to study the fluctuations of reaction time Y(n) time series, where n is the trial number. Methods Herein we propose treating each reaction time as a duration time that changes the representation from operational (trial number) time n to event (temporal) time t, or X(t). The DFA algorithm was then applied to the X(t) time series to evaluate scaling indices. The dataset analyzed is based on a Go–NoGo shooting task that was performed by 30 participants under low and high time‐stress conditions in each of six repeated sessions over a 3‐week period. Results This new perspective leads to quantitatively better results in (1) differentiating scaling indices between low versus high time‐stress conditions and (2) predicting task performance outcomes. Conclusion We show that by changing from operational time to event time, the DFA allows discrimination of time‐stress conditions and predicts performance outcomes.

We present an innovative method harnessing multistability within a diode-pumped erbium-doped fiber laser to construct logic gates. Our approach involves manipulating the intensity of external noise to regulate the probability of transitioning among four concurrent attractors. In this manner, we facilitate the realization of OR, AND, NOR, and NAND logic operations, aligning with the coexisting period-1, period-3, period-4, and period-5 orbits. Employing detrended fluctuation analysis, we establish equilibrium in the probability distributions of these states. The obtained results denote a substantial advancement in the field of optical logic gate development, representing a pivotal stride toward the seamless integration of an all-optical logic gate within laser oscillator-based systems.

Continuous adaptations of the movement system to changing environments or task demands rely on superposed fractal processes exhibiting power laws, that is, multifractality. The estimators of the multifractal spectrum potentially reflect the adaptive use of perception, cognition, and action. To observe time-specific behavior in multifractal dynamics, a multiscale multifractal analysis based on DFA (MFMS-DFA) has been recently proposed and applied to cardiovascular dynamics. Here we aimed at evaluating whether MFMS-DFA allows identifying multiscale structures in the dynamics of human movements. Thirty-six (12 females) participants pedaled freely, after a metronomic initiation of the cadence at 60 rpm, against a light workload for 10 min: in reference to cycling (C), cycling while playing “Tetris” on a computer, alone (CT) or collaboratively (CTC) with another pedaling participant. Pedal revolution periods (PRP) series were examined with MFMS-DFA and compared to linearized surrogates, which attested to a presence of multifractality at almost all scales. A marked alteration in multifractality when playing Tetris was evidenced at two scales, τ ≈ 16 and τ ≈ 64 s, yet less marked at τ ≈ 16 s when playing collaboratively. Playing Tetris in collaboration attenuated these alterations, especially in the best Tetris players. This observation suggests the high sensitivity to cognitive demand of MFMS-DFA estimators, extending to the assessment of skill/demand interplay from individual behavior. So, by identifying scale-dependent multifractal structures in movement dynamics, MFMS-DFA has obvious potential for examining brain-movement coordinative structures, likely with sufficient sensitivity to find echo in diagnosing disorders and monitoring the progress of diseases that affect cognition and movement control.

The temporal evolution of the global mean sea level (GMSL) is investigated in the present analysis using the monthly mean values obtained from two sources: a reconstructed dataset and a satellite altimeter dataset. To this end, we use two well-known techniques, detrended fluctuation analysis (DFA) and multifractal DFA (MF-DFA), to study the scaling properties of the time series considered. The main result is that power-law long-range correlations and multifractality apply to both data sets of the global mean sea level. In addition, the analysis revealed nearly identical scaling features for both the 134-year and the last 28-year GMSL-time series, possibly suggesting that the long-range correlations stem more from natural causes. This demonstrates that the relationship between climate change and sea-level anomalies needs more extensive research in the future due to the importance of their indirect processes for ecology and conservation.

Reciprocating compressors and centrifugal pumps are rotating machines used in industry, where fault detection is crucial for avoiding unnecessary and costly downtime. A novel method for fault classification in reciprocating compressors and multi-stage centrifugal pumps is proposed. In the feature extraction stage, raw vibration signals are processed using multi-fractal detrended fluctuation analysis (MFDFA) to extract features indicative of different types of faults. Such MFDFA features enable the training of machine learning models for classifying faults. Several classical machine learning models and a deep learning model corresponding to the convolutional neural network (CNN) are compared with respect to their classification accuracy. The cross-validation results show that all models are highly accurate for classifying the 13 types of faults in the centrifugal pump, the 17 valve faults, and the 13 multi-faults in the reciprocating compressor. The random forest subspace discriminant (RFSD) and the CNN model achieved the best results using MFDFA features calculated with quadratic approximations. The proposed method is a promising approach for fault classification in reciprocating compressors and multi-stage centrifugal pumps.

In this study we investigate possible long-range trends in the cryptocurrency market. We employed the Hurst exponent in a sample covering the period from 1 January 2016 to 26 March 2021. We calculated the Hurst exponent in three non-overlapping consecutive windows and in the whole sample. Using these windows, we assessed the dynamic evolution in the structure and long-range trend behavior of the cryptocurrency market and evaluated possible changes in their behavior towards an efficient market. The innovation of this research is that we employ the Hurst exponent to identify the long-range properties, a tool that is seldomly used in analysis of this market. Furthermore, the use of both the R/S and the DFA analysis and the use of non-overlapping windows enhance our research's novelty. Finally, we estimated the Hurst exponent for a wide sample of cryptocurrencies that covered more than 80% of the entire market for the last six years. The empirical results reveal that the returns follow a random walk making it difficult to accurately forecast them. [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]

Background: Breast hypertrophy is a significant health problem with both physiological and psychological impacts on the patients' lives. Patients with macromastia adopt a corrective posture due to the effect of the breast on the center of gravity and possibly in a subconscious effort to conceal their breasts. Objective: This study aimed to evaluate whether the posture of patients with macromastia changed after the reduction of mammoplasty. Material and Methods: In this prospective study, patients with breast cup sizes C, D, and DD were scheduled for reduction mammoplasty in 3 Shiraz University Hospitals. Age, weight, height, and preoperative cup sizes of the breasts were recorded for every patient, and all patients underwent posture analysis with forceplate before and after reduction mammoplasty. Finally, the preoperative and postoperative data were compared. Results: Mean age at the time of reduction mammaplasty was 43.57<±>9.1; the mean pre-operation, such as weight, height, and mean the body mass index (BMI) was 76.57<±>10 kg, 158.28<±>6 cm and 30.57<±>4.1, respectively. The average Anteriorposterior (AP) direction velocity before and after the surgery was 0.85<±>0.12 cm/s and 0.79<±>0.098, respectively. These values were 0.83<±>0.09 and 0.81<±>0.10 for the mediolateral direction. The Detrended Fluctuation Analysis (DFA) value for the AP direction was 1.63<±>0.3 and 1.60<±>0.2 for pre-and post-surgery, respectively, which was not statistically different. The DFA value for maximum likelihood (ML) direction was 1.65<±>0.2 and 1.48<±>0.2 in pre-op and post-op, respectively, which was statistically significantly different. Conclusion: Reducing the weight of enlarged breasts can correct disturbed sagittal balance and postural sway. [ABSTRACT FROM AUTHOR]

Proprioception is a crucial property for movement stability and balance, but its current assessment, based on clinical testing, lacks precision and adequacy in real contexts. This study proposes assessing proprioception and its sensitivity to training effects through acceleration time series recorded during two slackline experiments. In the first experiment, slackliners of different expertise (highly and poorly trained) had to walk on a slackline for 30 s. In the second, twelve beginners had to balance up on the slackline for at least 11 s before and after a training process. Acceleration time series were recorded in body components (legs and centre of mass) and the slackline. The acceleration fluctuations were analysed through Detrended Fluctuation Analysis. The obtained Hurst (H)-exponents were compared between both groups (first experiment) and before and after training (second experiment) using Whitney and Wilcoxon tests, respectively. The values of H-exponents were lower in the highly trained group (Z = -2.15, p = 0.03) (first experiment), and in the post-training conditions (Z = -2.35, p = 0.02) (second experiment). These results suggest better motor and proprioceptive control with training status. Hence, the time-variability structure of acceleration in real contexts, like slackline tasks, is proposed as an objective measure of proprioception and its training effects. [ABSTRACT FROM AUTHOR]

The present study investigated whether fractal dynamics can be observed during single-leg standing on a slackline. We also examined whether the temporal structure differs with skill level. To address these questions, we compared single-leg standing performance between novices (N=5) and experts (N=5) in terms of fractal dynamics in both ankles (i.e., stance and swing legs), center of mass, and head acceleration time series using detrended fluctuation analysis. Participants were required to perform single-leg standing on a slackline. To collect motion data while slacklining, we used a three-dimensional motion capture system and obtained time-series data on the position. We conducted detrended fluctuation analysis on the original acceleration time series and random shuffled time series to examine the fractal dynamics in each body part's fluctuation. Results suggest that experts showed persistent temporal structure in the swinging leg, center of mass, and head fluctuations in the horizontal direction, while that of novices did not differ from random fluctuations. These findings revealed that experts performing a single-leg standing task on a slackline show fractal dynamics. This might reflect their flexible or adaptive exploratory behavior in the performer-environment system and contribute to the dynamic stability of whole-body dynamic balancing. [ABSTRACT FROM AUTHOR]

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]

Profound research on the characteristics of the long-term persistence of wind is greatly significant for understanding the characteristics of wind speed mechanisms as well as for avoiding disasters caused by wind. In the current study, we selected the hourly 10 min wind speed series between 2017 and 2021 from 105 nation-level meteorological stations in Xinjiang and investigated the spatiotemporal variations in the long-term persistence of wind speed in different regions of Xinjiang and in different seasons using detrended fluctuation analysis. The main findings are as follows: (1) The wind speed in Xinjiang shows noticeable annual and seasonal variations, exhibiting satisfactory long-term sustainability. Winter has the best long-term sustainability, followed sequentially by spring, autumn, and summer because of wind speed stability. (2) The long-term persistence of hourly wind speed in Xinjiang exhibits remarkable regionality, with regions with strong wind superior to the remaining regions. (3) The long-term persistence of wind speed within the same season is primarily associated with wind speed magnitude and the dispersion degree between 90% and 100% of the wind speed numerical values. A higher wind speed indicates better long-term persistence. At the same speed, the more discrete the numerical values in the 90–100% distribution range, the better the persistence.

Proprioception is a crucial property for movement stability and balance, but its current assessment, based on clinical testing, lacks precision and adequacy in real contexts. This study proposes assessing proprioception and its sensitivity to training effects through acceleration time series recorded during two slackline experiments. In the first experiment, slackliners of different expertise (highly and poorly trained) had to walk on a slackline for 30 s. In the second, twelve beginners had to balance up on the slackline for at least 11 s before and after a training process. Acceleration time series were recorded in body components (legs and centre of mass) and the slackline. The acceleration fluctuations were analysed through Detrended Fluctuation Analysis. The obtained Hurst (H)-exponents were compared between both groups (first experiment) and before and after training (second experiment) using Whitney and Wilcoxon tests, respectively. The values of H-exponents were lower in the highly trained group (Z = −2.15, p = 0.03) (first experiment), and in the post-training conditions (Z = −2.35, p = 0.02) (second experiment). These results suggest better motor and proprioceptive control with training status. Hence, the time-variability structure of acceleration in real contexts, like slackline tasks, is proposed as an objective measure of proprioception and its training effects.

The persistence effect contribution of legacy nutrients is often cited as a reason for little or no improvement in water quality following extensive implementation of watershed nutrient mitigation actions, yet there is limited knowledge concerning factors influencing this response, often called the “persistence effect.” Here, we adopted detrended fluctuation analysis and Spearman analysis methods to assess the influence of land use on the watershed phosphorus (P) persistence effect, using monthly water quality records during 2010–2016 in 13 catchments within a drinking water reservoir watershed in eastern China. Detrended fluctuation analysis was used to calculate the Hurst exponent α to assess watershed legacy P characteristics (α ≈ 0.5, α > 0.5, and α

Based on the long‐range correlation (LRC) of daily air temperature records in China, the simulation performance of 11 Coupled Model Intercomparison Project Phase 6 (CMIP6) models is first evaluated in this study. And then, those CMIP6 models which can reproduce LRC of the well in observed records are selected for multi‐model ensemble mean. The results indicate that multi‐model ensemble mean can significantly reduce model uncertainty in reproducing LRC characteristics of daily air temperature records. Comparing the differences of the scaling exponents of the ensemble mean temperature data between the historical well‐mixed greenhouse gas, historical natural‐only, historical anthropogenic aerosols, and historical experiments, the LRC characteristics of different temperature variables show some difference. It means that the different forcings are critical for the model to reproduce the LRC characteristics of different temperature variables well. Generally, the influence of anthropogenic forcing enhances LRC of daily maximum air temperature in parts of northern China. Therefore, anthropogenic forcing is very important for CMIP6 models to accurately reproduce the strength of LRC for observed daily air maximum temperature in northern China. [ABSTRACT FROM AUTHOR]

Non-fungible tokens (NFTs) are a type of digital record of ownership used in a unique way: ensuring authenticity and uniqueness. Due to these characteristics, NFTs have been used in several markets: games, arts, and sports, among others. In 2020, the volume of negotiations of the NFTs was about USD 200 million. Despite the strong interest of economic agents in operating with NFTs, there are still gaps in the literature, regarding their dynamics and price interrelation with other potentially related assets, which deserve to be studied. In this sense, the main purpose in this paper is to analyze the cross-correlation between NFTs and larger cryptocurrencies. To this end, our methodological approach is based on a Detrended Cross-Correlation Analysis correlation coefficient, with a sliding windows approach. Our main finding is that the cross-correlations are not significant, except for a few cryptocurrencies, with weak significance at some moments of time. We also carried out an analysis of the long-term memory of NFTs, which demonstrated the antipersistence of these assets, with results seemingly corroborating the market inefficiency hypothesis. Our results are particularly important for different classes of investors, due to the analysis on different time scales. [ABSTRACT FROM AUTHOR]

Rangeland ecosystems comprise more than a third of the global land surface, sustaining essential ecosystem services and livelihoods. In Spain, Southeast Spain includes some of the driest regions; accordingly, rangelands from Murcia and Almeria provinces were selected for this study. We used time series metrics and the Hurst Exponent from rescale range and detrended fluctuation analysis to cluster different rangeland dynamics to classify temporally and spatially diverse rangelands. The metrics were only calculated for three time periods that showed significant NDVI changes: March to April, April to July, and September to December. Detrended fluctuation analysis was not previously employed to cluster vegetation. This study used it to improve rangeland classification. K-means and unsupervised random forest were used to cluster the pixels using time series metrics and Hurst exponents. The best clustering results were obtained when unsupervised random forest was used with the Hurst exponent calculated with detrended fluctuation analysis. We used the Silhouette Index to evaluate the clustering results and a spatial comparison with topographical data. Our results show that adding the Hurst exponent, calculated with detrended fluctuation analysis, provided a better classification when clustering NDVI time series, while classifications without the Hurst exponent or with the Hurst exponent calculated with the rescale range method showed lower silhouette values. Overall, this shows the importance of using detrending when calculating the Hurst exponent on vegetation time series, and its usefulness in studying rangeland dynamics for management and research. [ABSTRACT FROM AUTHOR]

People working as a team can achieve more than when working alone due to a team's ability to parallelize the completion of tasks. In collaborative search tasks, this necessitates the formation of effective division of labor strategies to minimize redundancies in search. For such strategies to be developed, team members need to perceive the task's relevant components and how they evolve over time, as well as an understanding of what others will do so that they can structure their own behavior to contribute to the team's goal. This study explored whether the capacity for team members to coordinate effectively can be related to how participants structure their search behaviors in an online multiplayer collaborative search task. Our results demonstrated that the structure of search behavior, quantified using detrended fluctuation analysis, was sensitive to contextual factors that limit a participant's ability to gather information. Further, increases in the persistence of movement fluctuations during search behavior were found as teams developed more effective coordinative strategies and were associated with better task performance. [ABSTRACT FROM AUTHOR]

Detrended fluctuation analysis (DFA) can quantify long-range correlation (LRC) and fractal scaling behavior of signal. We compared the results of variant DFA methods by varying the order of the polynomial and found that the order of 6 was relatively better than the others when both the accuracy and computational cost were taken into account. An alternative DFA method is proposed to quantify the LRC exponent by using best-fit polynomial algorithm in each segment instead of the polynomial of the same order in all of segments. In this study, the best-fit polynomial algorithm with the maximum order of 6 is used to fit the local trend in each segment to detrend the trend of a time series, and then the revised DFA is used to quantify the LRC in the time series. A series of numerical studies demonstrate that the best-fit DFA performs better than regular DFA, especially for the time series with scaling exponent smaller than 0.5. This may be attributed to the improvement of the fitted trend at the end of each segment. The estimation results of variant DFA methods reach stable when the time series length is greater than 1,000.

Nonlinear analyses of heart rate variability (HRV) can be used to quantify the unpredictability, fractal properties and complexity of heart rate. Fractality and its analysis provides valuable information about cardiovascular health. Multi-Scale Multi-Fractal Detrended Fluctuation Analysis (MSMFDFA) is a complexity-based algorithm that can be used to quantify the multi-fractal dynamics of the HRV time series through investigating characteristic exponents at different time scales. This method is applicable to short time series and it is robust to noise and nonstationarity. We have used MSMFDFA, which enables assessment of HRV in the frequency ranges encompassing the very-low frequency and ultra-low frequency bands, to jointly assess multi-scale and multi-fractal dynamics of HRV signals obtained from telemetric ECG recordings in wildtype mice at baseline and after autonomic nervous system (ANS) blockade, from electrograms recorded from isolated atrial preparations and from spontaneous action potential recordings in isolated sinoatrial node myocytes. Data demonstrate that the fractal profile of the intrinsic heart rate is significantly different from the baseline heart rate in vivo, and it is also altered after ANS blockade at specific scales and fractal order domains. For beating rate in isolated atrial preparations and intrinsic heart rate in vivo, the average fractal structure of the HRV increased and multi-fractality strength decreased. These data demonstrate that fractal properties of the HRV depend on both ANS activity and intrinsic sinoatrial node function and that assessing multi-fractality at different time scales is an effective approach for HRV assessment. [ABSTRACT FROM AUTHOR]

Simple Summary: Neural alterations following the warm-up phase of a physical activity program are not clearly established with linear-based methods. However, it appears that nonlinear/complexity-based methods are sensitive enough to detect small and subtle changes in the individuals' neural drive. Accordingly, the present exploratory study aimed to apply different complexity-based methods to surface electromyography (EMG) of hamstring muscles to detect changes in neural activation after a standardized warm-up and after stretching exercises. Maximal and sub-maximal contractions were performed before and after these activities. The resultant EMG signal was processed using a linear analysis (the root mean square) and using nonlinear analyses: sample entropy, the recurrence quantification analysis and the detrended fluctuation analysis. These methods are well known to witness the presence of informationally rich variability. Our results revealed an increase in complexity of the EMG signal after warm-up and stretching during both maximal and sub-maximal contractions. In contrast, the linear analysis did not show any alteration. We concluded that, as for neuromuscular fatigue, complexity-based methods are sensitive enough to detect subtle changes in the EMG signal. The so-observed increase in complexity after warm-up and stretching would suggest that the neuromuscular system is in an optimized state for subsequent neuromuscular activity. This study aimed to apply different complexity-based methods to surface electromyography (EMG) in order to detect neuromuscular changes after realistic warm-up procedures that included stretching exercises. Sixteen volunteers conducted two experimental sessions. They were tested before, after a standardized warm-up, and after a stretching exercise (static or neuromuscular nerve gliding technique). Tests included measurements of the knee flexion torque and EMG of biceps femoris (BF) and semitendinosus (ST) muscles. EMG was analyzed using the root mean square (RMS), sample entropy (SampEn), percentage of recurrence and determinism following a recurrence quantification analysis (%Rec and %Det) and a scaling parameter from a detrended fluctuation analysis. Torque was significantly greater after warm-up as compared to baseline and after stretching. RMS was not affected by the experimental procedure. In contrast, SampEn was significantly greater after warm-up and stretching as compared to baseline values. %Rec was not modified but %Det for BF muscle was significantly greater after stretching as compared to baseline. The a scaling parameter was significantly lower after warm-up as compared to baseline for ST muscle. From the present results, complexity-based methods applied to the EMG give additional information than linear-based methods. They appeared sensitive to detect EMG complexity increases following warm-up. [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]

Lidar is an active remote sensing technology that has many advantages, but the echo lidar signal is extremely susceptible to noise and complex atmospheric environment, which affects the effective detection range and retrieval accuracy. In this paper, a wavelet transform (WT) and locally weighted scatterplot smoothing (LOWESS) based on ensemble empirical mode decomposition (EEMD) for Rayleigh lidar signal denoising was proposed. The WT method was used to remove the noise in the signal with a signal-to-noise ratio (SNR) higher than 16 dB. The EEMD method was applied to decompose the remaining signal into a series of intrinsic modal functions (IMFs), and then detrended fluctuation analysis (DFA) was conducted to determine the threshold for distinguishing whether noise or signal was the main component of the IMFs. Moreover, the LOWESS method was adopted to remove the noise in the IMFs component containing the signal, and thus, finely extract the signal. The simulation results showed that the denoising effect of the proposed WT-EEMD-LOWESS method was superior to EEMD-WT, EEMD-SVD and VMD-WOA. Finally, the use of WT-EEMD-LOWESS on the measured lidar signal led to significant improvement in retrieval accuracy. The maximum error of density and temperature retrievals was decreased from 1.36% and 125.79 K to 1.1% and 13.84 K, respectively. [ABSTRACT FROM AUTHOR]

Background: Previous studies have associated slowed ventricular conduction with the arrhythmogenesis mediated by the gap junction and sodium channel inhibitor heptanol in mouse hearts. However, they did not study the propagation patterns that might contribute to the arrhythmic substrate. This study used a multi-electrode array mapping technique to further investigate different conduction abnormalities in Langendorff-perfused mouse hearts exposed to 0.1 or 2 mM heptanol. Methods: Recordings were made from the left ventricular epicardium using multi-electrode arrays in spontaneously beating hearts during right ventricular 8 Hz pacing or S1S2 pacing. Results: In spontaneously beating hearts, heptanol at 0.1 and 2 mM significantly reduced the heart rate from 314 ± 25 to 189 ± 24 and 157 ± 7 bpm, respectively (ANOVA, p < 0.05 and p < 0.001). During regular 8 Hz pacing, the mean LATs were increased by 0.1 and 2 mM heptanol from 7.1 ± 2.2 ms to 19.9 ± 5.0 ms (p < 0.05) and 18.4 ± 5.7 ms (p < 0.05). The standard deviation of the mean LATs was increased from 2.5 ± 0.8 ms to 10.3 ± 4.0 ms and 8.0 ± 2.5 ms (p < 0.05), and the median of phase differences was increased from 1.7 ± 1.1 ms to 13.9 ± 7.8 ms and 12.1 ± 5.0 ms by 0.1 and 2 mM heptanol (p < 0.05). P5 took a value of 0.2 ± 0.1 ms and was not significantly altered by heptanol at 0.1 or 2 mM (1.1 ± 0.9 ms and 0.9 ± 0.5 ms, p > 0.05). P50 was increased from 7.3 ± 2.7 ms to 24.0 ± 12.0 ms by 0.1 mM heptanol and then to 22.5 ± 7.5 ms by 2 mM heptanol (p < 0.05). P95 was increased from 1.7 ± 1.1 ms to 13.9 ± 7.8 ms by 0.1 mM heptanol and to 12.1 ± 5.0 ms by 2 mM heptanol (p < 0.05). These changes led to increases in the absolute inhomogeneity in conduction (P5–95) from 7.1 ± 2.6 ms to 31.4 ± 11.3 ms, 2 mM: 21.6 ± 7.2 ms, respectively (p < 0.05). The inhomogeneity index (P5–95/P50) was significantly reduced from 3.7 ± 1.2 to 3.1 ± 0.8 by 0.1 mM and then to 3.3 ± 0.9 by 2 mM heptanol (p < 0.05). Conclusion: Increased activation latencies, reduced CVs, and the increased inhomogeneity index of conduction were associated with both spontaneous and induced ventricular arrhythmias. [ABSTRACT FROM AUTHOR]

Abstract The respiratory system reacts instantaneously to intrinsic and extrinsic inputs. This adaptability results in significant fluctuations in breathing parameters, such as respiratory rate, tidal volume, and inspiratory flow profiles. Breathing variability is influenced by several conditions, including sleep, various pulmonary diseases, hypoxia, and anxiety disorders. Recent studies have suggested that weaning failure during mechanical ventilation may be predicted by low respiratory variability. This review describes methods for quantifying breathing variability, summarises the conditions and comorbidities that affect breathing variability, and discusses the potential implications of breathing variability for anaesthesia and intensive care.

Present study is aimed at investigating the solar faculae area from 1990 to 2007 which partially covered the 22nd and 23rd solar cycle. Rescaled Range Analysis (RRA) and Detrended Fluctuation Analysis (DFA) have been adopted to evaluate the behaviour of nonlinear dynamics of solar faculae area. Results show that the value of Hurst exponent for solar faculae area from RRA and DFA is negatively correlated. It means it is non-persistent and long-range correlated. Obtained result is inaccurate so the only solution is to transform the data into stationary data by taking differencing. RRA is applied on residuals and RRA to evaluate the fractal property of the time series. Solar faculae area investigated in this study is fractal in nature and predictable as well. Moreover, the time series of solar faculae area is non-linear as established by the Brock – Dechert – Scheinkman (BDS) test results

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.

The current study incorporates concepts from dynamicalsystems theory (DST) and embodied cognition topropose a novel method of answering traditionalquestions in social psychology. Namely, we wereinterested in understanding postural sway complexityduring the important interpersonal task of disclosing ahidden stigmatized identity (e.g., mental health disorder,history of sexual abuse). Using detrended fluctuationanalysis and multifractal detrended fluctuation analysis,we captured postural activity while people shared theirpersonal secrets to an imagined other. Results suggestthat disclosure context, defined by both disclosureconfidant and antecedent goals, is indeed embodied inour complex postural activity.

Nonlinear analyses of heart rate variability (HRV) can be used to quantify the unpredictability, fractal properties and complexity of heart rate. Fractality and its analysis provides valuable information about cardiovascular health. Multi-Scale Multi-Fractal Detrended Fluctuation Analysis (MSMFDFA) is a complexity-based algorithm that can be used to quantify the multi-fractal dynamics of the HRV time series through investigating characteristic exponents at different time scales. This method is applicable to short time series and it is robust to noise and nonstationarity. We have used MSMFDFA, which enables assessment of HRV in the frequency ranges encompassing the very-low frequency and ultra-low frequency bands, to jointly assess multi-scale and multi-fractal dynamics of HRV signals obtained from telemetric ECG recordings in wildtype mice at baseline and after autonomic nervous system (ANS) blockade, from electrograms recorded from isolated atrial preparations and from spontaneous action potential recordings in isolated sinoatrial node myocytes. Data demonstrate that the fractal profile of the intrinsic heart rate is significantly different from the baseline heart rate in vivo, and it is also altered after ANS blockade at specific scales and fractal order domains. For beating rate in isolated atrial preparations and intrinsic heart rate in vivo, the average fractal structure of the HRV increased and multi-fractality strength decreased. These data demonstrate that fractal properties of the HRV depend on both ANS activity and intrinsic sinoatrial node function and that assessing multi-fractality at different time scales is an effective approach for HRV assessment.

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]

In this paper, we thoroughly analyze the detection of sleep apnea events in the context of Obstructive Sleep Apnea (OSA), which is considered a public health problem because of its high prevalence and serious health implications. We especially evaluate patients who do not always show desaturations during apneic episodes (non-desaturating patients). For this purpose, we use a database (HuGCDN2014-OXI) that includes desaturating and non-desaturating patients, and we use the widely used Physionet Apnea Dataset for a meaningful comparison with prior work. Our system combines features extracted from the Heart-Rate Variability (HRV) and SpO2, and it explores their potential to characterize desaturating and non-desaturating events. The HRV-based features include spectral, cepstral, and nonlinear information (Detrended Fluctuation Analysis (DFA) and Recurrence Quantification Analysis (RQA)). SpO2-based features include temporal (variance) and spectral information. The features feed a Linear Discriminant Analysis (LDA) classifier. The goal is to evaluate the effect of using these features either individually or in combination, especially in non-desaturating patients. The main results for the detection of apneic events are: (a) Physionet success rate of 96.19%, sensitivity of 95.74% and specificity of 95.25% (Area Under Curve (AUC): 0.99); (b) HuGCDN2014-OXI of 87.32%, 83.81% and 88.55% (AUC: 0.934), respectively. The best results for the global diagnosis of OSA patients (HuGCDN2014-OXI) are: success rate of 95.74%, sensitivity of 100%, and specificity of 89.47%. We conclude that combining both features is the most accurate option, especially when there are non-desaturating patterns among the recordings under study.

Heart Rate Variability (HRV) can be a useful metric to capture meaningful information about heart function. One of the non-linear indices used to analyze HRV, Detrended Fluctuation Analysis (DFA), finds short and long-term correlations in RR intervals to capture quantitative information about variability. This study focuses on the impact of visual and mental stimulation on HRV as expressed via DFA within healthy adults. Visual stimulation can activate the automatic nervous system to directly impact physiological behavior such as heart rate. In this investigation of HRV, 70 participants (21 males) viewed images on a screen followed by a math and recall task. Each viewing segment lasted 2 min and 18 s. The math and memory recall task segment lasted 4 min total. This process was repeated 9 times during which the participants' electrocardiogram was recorded. 37 participants (12 males) opted in for an additional 24-h Holter recording after the viewing and task segments of the study were complete. Participants were randomly assigned to either a pure (organized image presentation) or mixed (random image presentation) image regime for the viewing portion of the study to investigate the impact of the external environment on HRV. DFA α 1 was extracted from the RR intervals. Our findings suggest that DFA α 1 can differentiate between the viewing [DFA α 1 range from 0.96 (SD = 0.25) to 1.08 (SD = 0.22)] and the task segments [DFA α 1 range from 1.17 (SD = 0.21) to 1.26 (SD = 0.25)], p < 0.0006 for all comparisons. However, DFA α 1 was not able to distinguish between the two image regimes. During the 24-hour follow up, participants had an average DFA α 1 = 1.09 (SD = 0.14). In conclusion, our findings suggest a graded response in DFA during short term stimulation and a responsiveness in participants to adjust physiologically to their external environment expressed through the DFA exponent. [ABSTRACT FROM AUTHOR]

A better understanding of past climate change is vital to our ability to predict possible future environmental dynamics. This study attempts to investigate the dynamic features of the temporal variability of peat humification, water table depth and air temperature by analyzing palaeoecological data from the Valdai Uplands region (Central European Russia). The regression analysis revealed the presence of a periodicity of about 6000 years in the reconstructed peat humification timeseries. Nonlinear analysis showed that humification time variability, water table depth and air temperature exhibit persistent long-range correlations of 1/f type. This indicates that a fluctuation in these variables in the past is very likely to be followed by a similar one in the future, but is magnified by 1/f power-law. In addition, it dictates that humification, water table depth and temperature are key parameters of a system that implies the existence of a special structure, such as self-organized criticality, operating close to a minimum stability configuration, and achieves it without any fine adjustment by external forcing. These conclusions point to new avenues for modeling future ecosystem disturbances and, in particular, for predicting relevant extreme events. [ABSTRACT FROM AUTHOR]

Vibration signals from centrifugal pumps are nonlinear, non-smooth, and possess implied trend terms, which makes it difficult for traditional signal processing methods to accurately extract their fault characteristics and details. With a view to rectifying this, we introduced empirical mode decomposition (EMD) to extract the trend term signals. These were then refit using the least squares (LS) method. The result (EMD-LS) was then combined with multi-fractal theory to form a new signal identification method (EMD-LS-MFDFA), whose accuracy was verified with a binomial multi-fractal sequence (BMS). Then, based on the centrifugal pump test platform, the vibration signals of shell failures under different degrees of cavitation and separate states of loosened foot bolts were collected. The signals' multi-fractal spectra parameters were analyzed using the EMD-LS-MFDFA method, from which five spectral parameters ( Δ α , Δ f , α 0 , α max , and α min ) were extracted for comparison and analysis. The results showed EMD-LS-MFDFA's performance was closer to the BMS theoretical value than that of MFDFA, displayed high accuracy, and was fully capable of revealing the multiple fractal characteristics of the centrifugal pump fault vibration signal. Additionally, the mean values of the five types of multi-fractal spectral characteristic parameters it extracted were much greater than the normal state values. This indicates that the parameters could effectively distinguish the normal state and fault state of the centrifugal pump. Moreover, α 0 and α max had a smaller mean square than Δ α , Δ f and α min , and their stability was higher. Thus, compared to the feature parameters extracted by MFDFA, our method could better realize the separation between the normal state, cavitation (whether slight, moderate, or severe), and when the anchor bolt was loose. This can be used to characterize centrifugal pump failure, quantify and characterize a pump's different working states, and provide a meaningful reference for the diagnosis and study of pump faults. [ABSTRACT FROM AUTHOR]

In this work, we compare three distinct schemes for detrended fluctuation analysis (DFA) of three-dimensional (3D) N × N × N data sets: (i) an N × N set of one-dimensional (1D) DFA runs for each vertical line of length N , (ii) a set of N two-dimensional (2D) DFA runs for each of the N × N planes along the vertical direction, and (iii) a single 3D DFA run. The objective of this work is two-fold: (i) to compare the performance of 1D, 2D, and 3D DFA methods in terms of compatibility as well as the richness of obtained results, and (ii) to demonstrate how these three complementary approaches shed new light on the complexity of the structure of the two real soil samples, through the analysis of grayscale high-resolution X-ray computed tomography scan images. [ABSTRACT FROM AUTHOR]

The complexity of the center of pressure (COP) provides important information regarding the underlying mechanisms of postural control. The relationships between COP complexity and balance performance are not fully established and might depend on the task constraints and the filtering decomposition of the COP signal. This study assessed COP complexity under different task constraints and it was assessed if emergent dynamics of COP fluctuations differ according to fractional components of COP related to peripheral or central adjustments. One hundred and sixty-two participants performed two sitting balance tasks. Accuracy was required by following a target that moved in the mediolateral (ML) or in the anteroposterior (AP) axis. Complexity dynamics of COP were addressed through detrended fluctuation analysis (DFA) in the axis constrained by accuracy requirements and in the one nonconstrained. Decomposition of COP components was applied by low-pass, band-pass, and high-pass filters. DFA of low-pass and band-pass components of COP in the constrained axis were small-to-moderately related (r = 0.190-0.237) to balance performance. DFA of the high-pass component of the COP exhibited the opposite relationship (r = -0.283 to -0.453) in both axes (constrained and nonconstrained). This study evidences that COP complexity is linked to better performance. This positive relationship complexity/performance is observed in the low- and mid-frequency components of the COP. These components might be related to central mechanisms of postural control. The lack of relationships between the different frequencies analyzed in the study suggests that they are capturing different components of postural control. [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]

Introduction: Although a few studies suggest that young overweight to obese children and adolescents (YO) may have impaired postural control compared to young normal-weight (YN) peers, little information exists about how these two groups differ in the quality of the underlying balance strategies employed. Hence, the aim of the present study was a first comprehensive examination of the structural complexity of postural sways in these two cohorts during quiet bilateral standing. Methods: Nineteen YO secondary school students (13.0 ± 1.4 years; male = 10, female = 9) were carefully matched to YN controls (13.0 ± 1.5 years) for age, sex, height, and school. Mediolateral (ML) and anteriorposterior (AP) acceleration signals were recorded with an inertial measurement unit (IMU) positioned at the trunk while standing barefoot in two conditions: firm and foam support surface. The magnitude of postural fluctuations was obtained using the root mean square (RMS). The temporal structure of the signals was analyzed via sample entropy (SEn), largest Lyapunov exponent (LyE), and detrended fluctuation analysis (α-DFA) algorithm. Reliability was assessed using a test–retest design. Results: In both groups, foam standing caused higher postural fluctuations (higher RMS values) and reduced structural complexity (lower SEn values, higher LyE values, higher α-DFA values). In comparison to YN, YO exhibited a higher RMSAP. Especially in ML direction, the acceleration signals of the YO had higher repeatability (smaller SEn values), greater long-range correlations (higher α-DFA values), and lower local stability (higher LyE values). However, these observations were largely independent of the task difficulty. Except for α-DFAAP, the IMU approach proved reliable to characterize posture control. Discussion: Our outcomes confirm postural control deficits in YO compared to their YN peers and indicate impaired regulatory mechanisms reflected as rigidity. Such less complex patterns usually reflect diverse pathologies, are detrimental to compensate for internal or external perturbations, and are attributed to lower adaptability and task performance. Without targeted balance stimuli, YO likely end in a lifelong vicious circle of mutually dependent poor balance regulation and low physical activity. [ABSTRACT FROM AUTHOR]