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Prof. Jianbo Gao
Beijing Normal University, China

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Research Keywords & Expertise

0 Medical Informatics
0 Multiscale Modeling
0 Nonlinear Dynamics
0 Time Series Analysis
0 Multiscale analysis of geophysical and geographic data

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Multiscale analysis of geophysical and geographic data

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Short Biography

Jianbo Gao received his Ph.D. in Electrical Engineering from UCLA in 2000, taught at the Electrical and Computer Engineering Department at the University of Florida (Gainesville), and collaborated with the Air Force Research Lab at Dayton Ohio, as a research Professor at Wright State University. He specializes in sophisticated physical and mathematical techniques to solve data-driven real-world problems in electrical engineering, bioengineering, finance, and the geo- and environmental sciences. He is a leading expert on multiscale analysis and nonlinear time series analysis. His book, "Multiscale Analysis of Complex Time Series: Integration of Chaos and Random Fractal Theory, and Beyond", is the first of its kind, and a highly praised book in the field. From 2004 to 2006, he was an associate editor of the IEEE Transactions on Biomedical Engineering and Signal Processing, and he recently joined the editorial board of Applied Sciences. He is now with Beijing Normal University, researching on dynamical evolution of world-wide political conflicts, modeling of global terrorism, forewarning of systemic risks and policy shocks in finance, and developing novel methods to analyze big geo-data.

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Review
Published: 21 June 2021 in Applied Sciences
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Mankind has long been fascinated by emergence in complex systems. With the rapidly accumulating big data in almost every branch of science, engineering, and society, a golden age for the study of complex systems and emergence has arisen. Among the many values of big data are to detect changes in system dynamics and to help science to extend its reach, and most desirably, to possibly uncover new fundamental laws. Unfortunately, these goals are hard to achieve using black-box machine-learning based approaches for big data analysis. Especially, when systems are not functioning properly, their dynamics must be highly nonlinear, and as long as abnormal behaviors occur rarely, relevant data for abnormal behaviors cannot be expected to be abundant enough to be adequately tackled by machine-learning based approaches. To better cope with these situations, we advocate to synergistically use mainstream machine learning based approaches and multiscale approaches from complexity science. The latter are very useful for finding key parameters characterizing the evolution of a dynamical system, including malfunctioning of the system. One of the many uses of such parameters is to design simpler but more accurate unsupervised machine learning schemes. To illustrate the ideas, we will first provide a tutorial introduction to complex systems and emergence, then we present two multiscale approaches. One is based on adaptive filtering, which is excellent at trend analysis, noise reduction, and (multi)fractal analysis. The other originates from chaos theory and can unify the major complexity measures that have been developed in recent decades. To make the ideas and methods better accessed by a wider audience, the paper is designed as a tutorial survey, emphasizing the connections among the different concepts from complexity science. Many original discussions, arguments, and results pertinent to real-world applications are also presented so that readers can be best stimulated to apply and further develop the ideas and methods covered in the article to solve their own problems. This article is purported both as a tutorial and a survey. It can be used as course material, including summer extensive training courses. When the material is used for teaching purposes, it will be beneficial to motivate students to have hands-on experiences with the many methods discussed in the paper. Instructors as well as readers interested in the computer analysis programs are welcome to contact the corresponding author.

ACS Style

Jianbo Gao; Bo Xu. Complex Systems, Emergence, and Multiscale Analysis: A Tutorial and Brief Survey. Applied Sciences 2021, 11, 5736 .

AMA Style

Jianbo Gao, Bo Xu. Complex Systems, Emergence, and Multiscale Analysis: A Tutorial and Brief Survey. Applied Sciences. 2021; 11 (12):5736.

Chicago/Turabian Style

Jianbo Gao; Bo Xu. 2021. "Complex Systems, Emergence, and Multiscale Analysis: A Tutorial and Brief Survey." Applied Sciences 11, no. 12: 5736.

Journal article
Published: 14 June 2021 in Materials
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It has long been a challenge to accurately and efficiently simulate exciton–phonon dynamics in mesoscale photosynthetic systems with a fully quantum mechanical treatment due to extensive computational resources required. In this work, we tackle this seemingly intractable problem by combining the Dirac–Frenkel time-dependent variational method with Davydov trial states and implementing the algorithm in graphic processing units. The phonons are treated on the same footing as the exciton. Tested with toy models, which are nanoarrays of the B850 pigments from the light harvesting 2 complexes of purple bacteria, the methodology is adopted to describe exciton diffusion in huge systems containing more than 1600 molecules. The superradiance enhancement factor extracted from the simulations indicates an exciton delocalization over two to three pigments, in agreement with measurements of fluorescence quantum yield and lifetime in B850 systems. With fractal analysis of the exciton dynamics, it is found that exciton transfer in B850 nanoarrays exhibits a superdiffusion component for about 500 fs. Treating the B850 ring as an aggregate and modeling the inter-ring exciton transfer as incoherent hopping, we also apply the method of classical master equations to estimate exciton diffusion properties in one-dimensional (1D) and two-dimensional (2D) B850 nanoarrays using derived analytical expressions of time-dependent excitation probabilities. For both coherent and incoherent propagation, faster energy transfer is uncovered in 2D nanoarrays than 1D chains, owing to availability of more numerous propagating channels in the 2D arrangement.

ACS Style

Fulu Zheng; Lipeng Chen; Jianbo Gao; Yang Zhao. Fully Quantum Modeling of Exciton Diffusion in Mesoscale Light Harvesting Systems. Materials 2021, 14, 3291 .

AMA Style

Fulu Zheng, Lipeng Chen, Jianbo Gao, Yang Zhao. Fully Quantum Modeling of Exciton Diffusion in Mesoscale Light Harvesting Systems. Materials. 2021; 14 (12):3291.

Chicago/Turabian Style

Fulu Zheng; Lipeng Chen; Jianbo Gao; Yang Zhao. 2021. "Fully Quantum Modeling of Exciton Diffusion in Mesoscale Light Harvesting Systems." Materials 14, no. 12: 3291.

Article
Published: 25 May 2021 in World Wide Web
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A good novel can often elicit from a reader strong sentiments similar to the moods, feelings, and attitudes depicted in the novel. With the rapid progress in AI, sentiment-based arcs in novels can now be reliably extracted and used to summarize the novel’s plot in the story arc. Are there salient mathematical properties that underlie such story arcs and have far-reaching implications in the writing, adaptation, and reading of the novel? To gain insights into this question, we employ multifractal theory to characterize the narrative coherence and dynamic evolution of sentiments of the novel, Never Let Me Go, by Kazuo Ishiguro, the winner of the 2017 Nobel Prize for Literature as an example. Three methods are compared for fractal scaling analysis, the classic variance-time method, an improvement of the variance-time relation based on adaptive filtering, and adaptive fractal analysis. We find that while variance-time relation fails to accurately extract the fractal scaling exponent, adaptive fractal analysis succeeds in fully characterizing the fractal variations in the sentiment dynamics. The finding may be indicative of the potential that multifractal theory has for computational narratology and large-scale literary analysis, especially for inferring the degree of narrative coherence and variation of the plot of a novel.

ACS Style

Qiyue Hu; Bin Liu; Jianbo Gao; Kristoffer L. Nielbo; Mads Rosendahl Thomsen. Fractal scaling laws for the dynamic evolution of sentiments in Never Let Me Go and their implications for writing, adaptation and reading of novels. World Wide Web 2021, 1 -18.

AMA Style

Qiyue Hu, Bin Liu, Jianbo Gao, Kristoffer L. Nielbo, Mads Rosendahl Thomsen. Fractal scaling laws for the dynamic evolution of sentiments in Never Let Me Go and their implications for writing, adaptation and reading of novels. World Wide Web. 2021; ():1-18.

Chicago/Turabian Style

Qiyue Hu; Bin Liu; Jianbo Gao; Kristoffer L. Nielbo; Mads Rosendahl Thomsen. 2021. "Fractal scaling laws for the dynamic evolution of sentiments in Never Let Me Go and their implications for writing, adaptation and reading of novels." World Wide Web , no. : 1-18.

Journal article
Published: 02 April 2021 in Sustainability
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Political risk assessment has become increasingly important in recent years, especially with the launch of the Belt and Road Initiative (BRI) and with Covid-19 still ravaging the world. This study aims to assess systematically the political risk of BRI countries during the period from 2013 to 2019 based on three big data sets, the Global Database of Events, Language, and Tone (GDELT), China Global Investment Tracker (CGIT), and Armed Conflict Location & Event Data Project (ACLED). It is found that to properly quantify the political risks for BRI countries, the type of events, “Material Conflict”, and a variable characterizing the degree of cooperation/conflicts of the events, the Goldstein Scale, are of critical importance. Based on the chosen type of events and variable, we design a normalized variable to assess political risk of any country in any year so that comparison among different countries can be meaningly made. By decomposing political risk into two components, domestic and international, and examining the spatiotemporal evolution of political risk along the Belt and Road, we find that the sum of the number of BRI countries with the extremely high level and the high level of domestic, international, and (overall) political risk all reached the peak in 2015, and decreased thereafter, and that often the level of domestic political risk along the Belt and Road was higher than the international political risk. It is also found that a strong positive correlation exists between political risk and China’s total investments and construction contracts along the Belt and Road during this period. The implications of this positive correlation are discussed. The analysis presented here may help to promote the sustainable development of BRI, and be extended to examine the risks associated with foreign investments other than BRI projects.

ACS Style

Xiaohui Sun; Jianbo Gao; Bin Liu; Zhenzhen Wang. Big Data-Based Assessment of Political Risk along the Belt and Road. Sustainability 2021, 13, 3935 .

AMA Style

Xiaohui Sun, Jianbo Gao, Bin Liu, Zhenzhen Wang. Big Data-Based Assessment of Political Risk along the Belt and Road. Sustainability. 2021; 13 (7):3935.

Chicago/Turabian Style

Xiaohui Sun; Jianbo Gao; Bin Liu; Zhenzhen Wang. 2021. "Big Data-Based Assessment of Political Risk along the Belt and Road." Sustainability 13, no. 7: 3935.

Journal article
Published: 06 January 2020 in Entropy
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How different are the emerging and the well-developed stock markets in terms of efficiency? To gain insights into this question, we compared an important emerging market, the Chinese stock market, and the largest and the most developed market, the US stock market. Specifically, we computed the Lempel–Ziv complexity (LZ) and the permutation entropy (PE) from two composite stock indices, the Shanghai stock exchange composite index (SSE) and the Dow Jones industrial average (DJIA), for both low-frequency (daily) and high-frequency (minute-to-minute)stock index data. We found that the US market is basically fully random and consistent with efficient market hypothesis (EMH), irrespective of whether low- or high-frequency stock index data are used. The Chinese market is also largely consistent with the EMH when low-frequency data are used. However, a completely different picture emerges when the high-frequency stock index data are used, irrespective of whether the LZ or PE is computed. In particular, the PE decreases substantially in two significant time windows, each encompassing a rapid market rise and then a few gigantic stock crashes. To gain further insights into the causes of the difference in the complexity changes in the two markets, we computed the Hurst parameter H from the high-frequency stock index data of the two markets and examined their temporal variations. We found that in stark contrast with the US market, whose H is always close to 1/2, which indicates fully random behavior, for the Chinese market, H deviates from 1/2 significantly for time scales up to about 10 min within a day, and varies systemically similar to the PE for time scales from about 10 min to a day. This opens the door for large-scale collective behavior to occur in the Chinese market, including herding behavior and large-scale manipulation as a result of inside information.

ACS Style

Jianbo Gao; Yunfei Hou; Fangli Fan; Feiyan Liu. Complexity Changes in the US and China’s Stock Markets: Differences, Causes, and Wider Social Implications. Entropy 2020, 22, 75 .

AMA Style

Jianbo Gao, Yunfei Hou, Fangli Fan, Feiyan Liu. Complexity Changes in the US and China’s Stock Markets: Differences, Causes, and Wider Social Implications. Entropy. 2020; 22 (1):75.

Chicago/Turabian Style

Jianbo Gao; Yunfei Hou; Fangli Fan; Feiyan Liu. 2020. "Complexity Changes in the US and China’s Stock Markets: Differences, Causes, and Wider Social Implications." Entropy 22, no. 1: 75.

Proceedings
Published: 17 November 2019 in Proceedings
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Systemic risks have to be vigilantly guided against at all times in order to prevent their contagion across stock markets. New policies also may not work as desired and even induce shocks to market, especially those emerging ones. Therefore, timely detection of systemic risks and policy-induced shocks is crucial to safeguard the health of stock markets. In this paper, we show that the relative entropy or Kullback–Liebler divergence can be used to identify systemic risks and policy-induced shocks in stock markets. Concretely, we analyzed the minutely data of two stock indices, the Dow Jones Industrial Average (DJIA) and the Shanghai Stock Exchange (SSE) Composite Index, and examined the temporal variation of relative entropy for them. We show that clustered peaks in relative entropy curves can accurately identify the timing of the 2007–2008 global financial crisis and its precursors, and the 2015 stock crashes in China. Moreover, a sharpest needle-like peak in relative entropy curves, especially for the SSE market, always served as a precursor of an unusual market, a strong bull market or a bubble, thus possessing a certain ability of forewarning.

ACS Style

Feiyan Liu; Jianbo Gao; Yunfei Hou. Identifying Systemic Risks and Policy-Induced Shocks in Stock Markets by Relative Entropy. Proceedings 2019, 46, 24 .

AMA Style

Feiyan Liu, Jianbo Gao, Yunfei Hou. Identifying Systemic Risks and Policy-Induced Shocks in Stock Markets by Relative Entropy. Proceedings. 2019; 46 (1):24.

Chicago/Turabian Style

Feiyan Liu; Jianbo Gao; Yunfei Hou. 2019. "Identifying Systemic Risks and Policy-Induced Shocks in Stock Markets by Relative Entropy." Proceedings 46, no. 1: 24.

Journal article
Published: 29 January 2019 in International Economics
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The Balassa Index (BI) is a widely used index for evaluating a country's trade (dis) advantage or specialization. Unsatisfied with its unstable distribution and poor ordinal ranking property, which arise from its unstable mean, asymmetric distributional shape, skewness and variable upper bound, many alternatives of BI, including Logarithm of RCA (LRCA), Revealed Symmetric Comparative Advantage (RSCA), Additive RCA (ARCA), Weighted RCA (WRCA), Normalized RCA (NRCA), B∗, and RCAi,k based on a micro-founded Ricardian model, have been proposed in the past several decades. One guiding principle in constructing new indices is that the distribution follows as much as possible a Gaussian. However, this goal has never been satisfactorily realized. To understand the cause, we have systematically carried out empirical analysis of exports within and across countries. We find that the exports of all the goods of a country, as well as a fixed good exported by all the countries in the world follow exponentially truncated Zipf-Mandelbrot's law, after ranked in descending order. The BI amounts to be the ratio of two such distributions, one in the naturally descending order of the exponentially truncated Zipf-Mandelbrot’ law, the other being a permutation of the Zipf-Mandelbrot's law with truncation (possibly with different parameters). Only in very rare situations can these ratios follow a Gaussian distribution. We thus shed light on why BI and its alternatives may have unstable mean for different goods or countries, asymmetric distributional shape, skewness and variable upper bound. In particular, the last feature is a natural consequence of the log-normal distribution of BI, which we find to likely occur in certain situations.

ACS Style

Bin Liu; Jianbo Gao. Understanding the non-Gaussian distribution of revealed comparative advantage index and its alternatives. International Economics 2019, 158, 1 -11.

AMA Style

Bin Liu, Jianbo Gao. Understanding the non-Gaussian distribution of revealed comparative advantage index and its alternatives. International Economics. 2019; 158 ():1-11.

Chicago/Turabian Style

Bin Liu; Jianbo Gao. 2019. "Understanding the non-Gaussian distribution of revealed comparative advantage index and its alternatives." International Economics 158, no. : 1-11.

Conference paper
Published: 14 June 2018 in Privacy Enhancing Technologies
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Everyone in a civilized society grows up by reading stories. Fictions, including those for children, are an important type of stories, as they reflect social and cultural reality to some degree. The plot, the figures, and the environment of a fiction are the three main elements of a fiction. In particular, the development of the plot is pivotal for a fiction to be successful. It is now generally thought that sentiment dynamics of the fiction can well reflect the plot development. With the availability of a number of algorithms to automatically obtain the sentiment dynamics of a fiction, it has become increasingly desirable to fully understand its sentiment dynamics. This motivates us to use random fractal theory to study a set of popular children’s fictions, The Chronicles of Narnia, written by the famed author, C.S. Lewis. We find the sentiment dynamics of each novel of the series possesses persistent long-range correlations, characterized by a Hurst parameter larger than 1/2. This has offered a mechanism to understand why many sentiment dynamics occurring naturally in a society or imagined by an author of a fiction can arouse strong emotions in humans. Interestingly, the value of the Hurst parameter for the series is strongly positively correlated with the score of the novels from Goodreads, suggesting that the scaling law governing sentiment dynamics can be used to objectively appraise the optimality of a fiction.

ACS Style

Kaiyun Dai; Menglan Ma; Jianbo Gao. Sentiment Dynamics of The Chronicles of Narnia and Their Ranking. Privacy Enhancing Technologies 2018, 213 -219.

AMA Style

Kaiyun Dai, Menglan Ma, Jianbo Gao. Sentiment Dynamics of The Chronicles of Narnia and Their Ranking. Privacy Enhancing Technologies. 2018; ():213-219.

Chicago/Turabian Style

Kaiyun Dai; Menglan Ma; Jianbo Gao. 2018. "Sentiment Dynamics of The Chronicles of Narnia and Their Ranking." Privacy Enhancing Technologies , no. : 213-219.

Journal article
Published: 24 September 2017 in Entropy
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Financial time series analyses have played an important role in developing some of the fundamental economic theories. However, many of the published analyses of financial time series focus on long-term average behavior of a market, and thus shed little light on the temporal evolution of a market, which from time to time may be interrupted by stock crashes and financial crises. Consequently, in terms of complexity science, it is still unknown whether the market complexity during a stock crash decreases or increases. To answer this question, we have examined the temporal variation of permutation entropy (PE) in Chinese stock markets by computing PE from high-frequency composite indies of two stock markets: the Shanghai Stock Exchange (SSE) and the Shenzhen Stock Exchange (SZSE). We have found that PE decreased significantly in two significant time windows, each encompassing a rapid market rise and then a few gigantic stock crashes. One window started in the middle of 2006, long before the 2008 global financial crisis, and continued up to early 2011. The other window was more recent, started in the middle of 2014, and ended in the middle of 2016. Since both windows were at least one year long, and proceeded stock crashes by at least half a year, the decrease in PE can be invaluable warning signs for regulators and investors alike.

ACS Style

Yunfei Hou; Feiyan Liu; Jianbo Gao; Changxiu Cheng; Changqing Song. Characterizing Complexity Changes in Chinese Stock Markets by Permutation Entropy. Entropy 2017, 19, 514 .

AMA Style

Yunfei Hou, Feiyan Liu, Jianbo Gao, Changxiu Cheng, Changqing Song. Characterizing Complexity Changes in Chinese Stock Markets by Permutation Entropy. Entropy. 2017; 19 (10):514.

Chicago/Turabian Style

Yunfei Hou; Feiyan Liu; Jianbo Gao; Changxiu Cheng; Changqing Song. 2017. "Characterizing Complexity Changes in Chinese Stock Markets by Permutation Entropy." Entropy 19, no. 10: 514.

Conference paper
Published: 12 June 2016 in Computer Vision
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ACS Style

Peng Fang; Jianbo Gao; Fangli Fan; Luhai Yang. Identifying Political “hot” Spots Through Massive Media Data Analysis. Computer Vision 2016, 282 -290.

AMA Style

Peng Fang, Jianbo Gao, Fangli Fan, Luhai Yang. Identifying Political “hot” Spots Through Massive Media Data Analysis. Computer Vision. 2016; ():282-290.

Chicago/Turabian Style

Peng Fang; Jianbo Gao; Fangli Fan; Luhai Yang. 2016. "Identifying Political “hot” Spots Through Massive Media Data Analysis." Computer Vision , no. : 282-290.

Original research article
Published: 02 June 2015 in Frontiers in Computational Neuroscience
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Multiscale entropy (MSE) analysis is an interesting method for analyzing biological signals. So far, however, few analytic results for MSE have been reported. This has severely limited our basic understanding of MSE. To overcome this limitation, and more importantly, to guide more fruitful applications of MSE in various areas of life sciences, we derive, for time series with long memory, a fundamental bi-scaling law, one for the scale in the phase space, the other for the block size used for smoothing. We illustrate the usefulness of the approach by examining heart rate variability (HRV) data for the purpose of distinguishing healthy subjects from patients with congestive heart failure, a life-threatening condition. 1

ACS Style

Jianbo Egao; Jing Hu; Feiyan Liu; Yinhe Cao. Multiscale entropy analysis of biological signals: a fundamental bi-scaling law. Frontiers in Computational Neuroscience 2015, 9, 64 .

AMA Style

Jianbo Egao, Jing Hu, Feiyan Liu, Yinhe Cao. Multiscale entropy analysis of biological signals: a fundamental bi-scaling law. Frontiers in Computational Neuroscience. 2015; 9 ():64.

Chicago/Turabian Style

Jianbo Egao; Jing Hu; Feiyan Liu; Yinhe Cao. 2015. "Multiscale entropy analysis of biological signals: a fundamental bi-scaling law." Frontiers in Computational Neuroscience 9, no. : 64.

Journal article
Published: 29 August 2013 in Entropy
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What is information? What role does information entropy play in this information exploding age, especially in understanding emergent behaviors of complex systems? To answer these questions, we discuss the origin of information entropy, the difference between information entropy and thermodynamic entropy, the role of information entropy in complexity theories, including chaos theory and fractal theory, and speculate new fields in which information entropy may play important roles.

ACS Style

Jianbo Gao; Feiyan Liu; Jianfang Zhang; Jing Hu; Yinhe Cao. Information Entropy As a Basic Building Block of Complexity Theory. Entropy 2013, 15, 3396 -3418.

AMA Style

Jianbo Gao, Feiyan Liu, Jianfang Zhang, Jing Hu, Yinhe Cao. Information Entropy As a Basic Building Block of Complexity Theory. Entropy. 2013; 15 (12):3396-3418.

Chicago/Turabian Style

Jianbo Gao; Feiyan Liu; Jianfang Zhang; Jing Hu; Yinhe Cao. 2013. "Information Entropy As a Basic Building Block of Complexity Theory." Entropy 15, no. 12: 3396-3418.

Editorial article
Published: 01 January 2013 in Frontiers in Physiology
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Fractal analyses: statistical and methodological innovations and best practices

ACS Style

John G. Holden; Michael A. Riley; Jianbo Gao; Kjerstin Torre. Fractal analyses: statistical and methodological innovations and best practices. Frontiers in Physiology 2013, 4, 97 .

AMA Style

John G. Holden, Michael A. Riley, Jianbo Gao, Kjerstin Torre. Fractal analyses: statistical and methodological innovations and best practices. Frontiers in Physiology. 2013; 4 ():97.

Chicago/Turabian Style

John G. Holden; Michael A. Riley; Jianbo Gao; Kjerstin Torre. 2013. "Fractal analyses: statistical and methodological innovations and best practices." Frontiers in Physiology 4, no. : 97.

Original research article
Published: 01 January 2013 in Frontiers in Physiology
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Heart rate variability (HRV) is highly non-stationary, even if no perturbing influences can be identified during the recording of the data. The non-stationarity becomes more profound when HRV data are measured in intrinsically non-stationary environments, such as social stress. In general, HRV data measured in such situations are more difficult to analyze than those measured in constant environments. In this paper, we analyze HRV data measured during a social stress test using two multiscale approaches, the adaptive fractal analysis (AFA) and scale-dependent Lyapunov exponent (SDLE), for the purpose of uncovering differences in HRV between chronic fatigue syndrome (CFS) patients and their matched-controls. CFS is a debilitating, heterogeneous illness with no known biomarker. HRV has shown some promise recently as a non-invasive measure of subtle physiological disturbances and trauma that are otherwise difficult to assess. If the HRV in persons with CFS are significantly different from their healthy controls, then certain cardiac irregularities may constitute good candidate biomarkers for CFS. Our multiscale analyses show that there are notable differences in HRV between CFS and their matched controls before a social stress test, but these differences seem to diminish during the test. These analyses illustrate that the two employed multiscale approaches could be useful for the analysis of HRV measured in various environments, both stationary and non-stationary.

ACS Style

Jianbo Gao; Brian M. Gurbaxani; Jing Hu; Keri J. Heilman; Vincent A. Emanuele Ii; Gregory F. Lewis; Maria Davila; Elizabeth R. Unger; Jin-Mann S. Lin. Multiscale analysis of heart rate variability in non-stationary environments. Frontiers in Physiology 2013, 4, 119 .

AMA Style

Jianbo Gao, Brian M. Gurbaxani, Jing Hu, Keri J. Heilman, Vincent A. Emanuele Ii, Gregory F. Lewis, Maria Davila, Elizabeth R. Unger, Jin-Mann S. Lin. Multiscale analysis of heart rate variability in non-stationary environments. Frontiers in Physiology. 2013; 4 ():119.

Chicago/Turabian Style

Jianbo Gao; Brian M. Gurbaxani; Jing Hu; Keri J. Heilman; Vincent A. Emanuele Ii; Gregory F. Lewis; Maria Davila; Elizabeth R. Unger; Jin-Mann S. Lin. 2013. "Multiscale analysis of heart rate variability in non-stationary environments." Frontiers in Physiology 4, no. : 119.

Journal article
Published: 15 February 2012 in Journal of The Royal Society Interface
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Culturomics was recently introduced as the application of high-throughput data collection and analysis to the study of human culture. Here, we make use of these data by investigating fluctuations in yearly usage frequencies of specific words that describe social and natural phenomena, as derived from books that were published over the course of the past two centuries. We show that the determination of the Hurst parameter by means of fractal analysis provides fundamental insights into the nature of long-range correlations contained in the culturomic trajectories, and by doing so offers new interpretations as to what might be the main driving forces behind the examined phenomena. Quite remarkably, we find that social and natural phenomena are governed by fundamentally different processes. While natural phenomena have properties that are typical for processes with persistent long-range correlations, social phenomena are better described as non-stationary, on–off intermittent or Lévy walk processes.

ACS Style

Jianbo Gao; Jing Hu; Xiang Mao; Matjaž Perc. Culturomics meets random fractal theory: insights into long-range correlations of social and natural phenomena over the past two centuries. Journal of The Royal Society Interface 2012, 9, 1956 -1964.

AMA Style

Jianbo Gao, Jing Hu, Xiang Mao, Matjaž Perc. Culturomics meets random fractal theory: insights into long-range correlations of social and natural phenomena over the past two centuries. Journal of The Royal Society Interface. 2012; 9 (73):1956-1964.

Chicago/Turabian Style

Jianbo Gao; Jing Hu; Xiang Mao; Matjaž Perc. 2012. "Culturomics meets random fractal theory: insights into long-range correlations of social and natural phenomena over the past two centuries." Journal of The Royal Society Interface 9, no. 73: 1956-1964.