Notes

Artery calcification, brittle bones, along with plasma tv's adiponectin amounts in China aging adults.
The occurrence of motifs is found to be one order of magnitude higher than in a random Erdős-Rényi network. This emphasizes the importance of local interaction structures for the emergence of global cascades and the stability of the network as a whole.Lean blowout (LBO) is a serious issue in modern gas turbine engines that operate in a lean (premixed) mode to follow the stringent emission norms. When an engine operates with a lean fuel-air mixture, the flame becomes unstable and is at times carried out of the combustion chamber by the unburnt flow. Thus, the sudden loss of the flame, known as lean blowout, leads to fatal accidents in aircrafts and loss of production in power plants. Therefore, an in-depth analysis of lean blowout is necessary as the phenomenon involves complex interactions between flow dynamics and chemical kinetics. For understanding the complex dynamics of this phenomenon, recurrence analysis can be a very useful method. In the current study, we observe a transition to LBO as the global fuel-air ratio is reduced from stoichiometric condition and perform recurrence quantification analysis (RQA) with the CH∗ chemiluminescence data obtained experimentally. The extent of fuel-air mixing is varied with an objective of developing some robust early predictors of LBO that would work over a wide range of premixing. We find some RQA measures, such as determinism, laminarity, and trapping time, which show distinctive signature toward LBO and thereby can be used as early predictors of LBO for both premixed and partially premixed flames. Our analysis shows that the computational time for laminarity and trapping time is relatively less. However, computational time for those measures depends upon the dynamics of the combustor, size of the data taken, and choice of recurrence threshold.In the classical three rotor problem, three equal point masses move on a circle subject to attractive cosine potentials of strength g. In the center of mass frame, energy E is the only known conserved quantity. In earlier works [Krishnaswami and Senapati, Indian Acad. Sci. Conf. Ser. 2(1), 139 (2019), and Chaos 29(12), 123121 (2019)], an order-chaos-order transition was discovered in this system along with a band of global chaos for 5.33g≤E≤5.6g. Here, we provide numerical evidence for ergodicity and mixing in this band. The distributions of relative angles and angular momenta along generic trajectories are shown to approach the corresponding distributions over constant energy hypersurfaces (weighted by the Liouville measure) as a power-law in time. S-Adenosylmethionine Moreover, trajectories emanating from a small volume are shown to become uniformly distributed over constant energy hypersurfaces, indicating that the dynamics is mixing. Outside this band, ergodicity and mixing fail, though the distributions of angular momenta over constant energy hypersurfaces show interesting phase transitions from Wignerian to bimodal with increasing energy. Finally, in the band of global chaos, the distribution of recurrence times to finite size cells is found to follow an exponential law with the mean recurrence time satisfying a scaling law involving an exponent consistent with global chaos and ergodicity.The theory of multistate template-directed reversible copolymerization is developed by extending the method based on iterated function systems to matrices, taking into account the possibility of multiple activation states instead of a single one for the growth process. In this extended theory, the mean growth velocity is obtained with an iterated matrix function system and the probabilities of copolymer sequences are given by matrix products defined along the template. The theory allows us to understand the effects of template heterogeneity, which include a fractal distribution of local growth velocities far enough from equilibrium, and a regime of sublinear growth in time close to equilibrium.Early afterdepolarization (EAD) is a major arrhythmogenic factor in the long QT syndrome (LQTS), whose conditions for genesis have puzzled people for several decades. Here, we employ the phase I Luo-Rudy ventricular myocyte model to investigate EAD using methods from nonlinear dynamics and provide valuable insights into EAD genesis from a physical perspective. Two major results are obtained (i) Sufficient parametric conditions for EAD are analytically determined and then used to analyze in detail the effects of the physiological parameters. (ii) The normal form of the Hopf bifurcation that leads to EAD is derived and then used to determine whether the Hopf bifurcation is subcritical or supercritical for EAD genesis and the corresponding amplitude and period of the EAD oscillation. Our work here paves the way for further studies of more complicated multi-scale dynamics of EAD and may lead to effective treatments for LQTS arrhythmias.Stochastic resonance (SR) is widely used as a signal enhancement technique in recovering and enhancing periodic or aperiodic signals submerged in noise. However, system parameters and noise intensity tend to influence the SR performance. To achieve better resonance performance, several indices are often used to determine these parameters, including signal-to-noise, amplification factor, and cross-correlation coefficient. Nevertheless, for a linear frequency modulated (LFM) signal, such indices may no longer work and consequently make SR unable to recover the unknown LFM signal from raw signals. Thus, this limits the application of SR to some extent. To deal with this problem, we define here a new index to characterize the unknown LFM signal with the help of the fractional Fourier transform. Guided by this index, SR is thus able to recover the unknown LFM signal from the raw signal. In addition, a cloud model based genetic algorithm is used to achieve an adaptive SR in order to improve the effectiveness of signal processing.Studying natural phenomena via the complex network approach makes it possible to quantify the time-evolving structures with too many elements and achieve a deeper understanding of interactions among the components of a system. In this sense, solar flare as a complex system with the chaotic behavior could be better characterized by the network parameters. Here, we employed an unsupervised network-based method to recognize the position and occurrence time of the solar flares by using the ultraviolet emission (1600 Å) recorded by the Atmospheric Imaging Assembly on board Solar Dynamics Observatory. Three different regions, the flaring active regions, the non-flaring active regions, and the quiet-Sun regions, were considered to study the variations of the network parameters in the presence and absence of flaring phases in various datasets over time intervals of several hours. The whole parts of the selected datasets were partitioned into sub-windows to construct networks based on computing the Pearson correlation between time series of the region of interest and intensities.
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