Chaos, Solitons and Fractals: X最新文献

When the novel coronavirus disease SARS-CoV2 (COVID-19) was officially declared a pandemic by the WHO in March 2020, the scientific community had already braced up in the effort of making sense of the fast-growing wealth of data gathered by national authorities all over the world. However, despite the diversity of novel theoretical approaches and the comprehensiveness of many widely established models, the official figures that recount the course of the outbreak still sketch a largely elusive and intimidating picture. Here we show unambiguously that the dynamics of the COVID-19 outbreak belongs to the simple universality class of the SIR model and extensions thereof. Our analysis naturally leads us to establish that there exists a fundamental limitation to any theoretical approach, namely the unpredictable non-stationarity of the testing frames behind the reported figures. However, we show how such bias can be quantified self-consistently and employed to mine useful and accurate information from the data. In particular, we describe how the time evolution of the reporting rates controls the occurrence of the apparent epidemic peak, which typically follows the true one in countries that were not vigorous enough in their testing at the onset of the outbreak. The importance of testing early and resolutely appears as a natural corollary of our analysis, as countries that tested massively at the start clearly had their true peak earlier and less deaths overall.

In a quantum gravity environment, the processes and events are causally non-separable because the term of time and the time-steps have no interpretable meaning in a non-fixed causality structure. Here, we study the energy transfer and thermodynamics of quantum gravity computations. We show that a non-fixed causality stimulates entropy transfer between the quantum gravity environment and the independent local systems of the quantum gravity space. We prove that the entropy transfer reduces the entropies of the contributing local systems and increases the entropy of the quantum gravity environment. We reveal on a smooth Cauchy slice that the space-time geometry of the quantum gravity environment dynamically adapts to the vanishing causality. We define the corresponding Hamiltonians and the causal development of the quantum gravity environment.

We quantify how incorporating physics into neural network design can significantly improve the learning and forecasting of dynamical systems, even nonlinear systems of many dimensions. We train conventional and Hamiltonian neural networks on increasingly difficult dynamical systems and compute their forecasting errors as the number of training data and number of system dimensions vary. A map-building perspective elucidates the superiority of Hamiltonian neural networks. The results clarify the critical relation among data, dimension, and neural network learning performance.

The construction of a continuous family of distributions on a compact set is demonstrated by concatenating, in a continuous manner, three probability density functions with bounded support using a modified mixture technique. The construction technique is similar to that of generalized trapezoidal (GT) distributions, but contrary to GT distributions, the resulting density function is smooth within its bounded domain. The construction of Generalized Trapezoidal Ogive (GTO) distributions was motivated by the COVID-19 epidemic, where smoothness of an infection rate curve may be a desirable property combined with the ability to separately model three stages and their durations as the epidemic progresses, being: (1) an increasing infection rate stage, (2) an infection rate stage of some stability and (3) a decreasing infection rate stage. The resulting model allows for asymmetry of the infection rate curve opposite to, for example, the Gaussian Error Infection (GEI) rate curve utilized early on for COVID-19 epidemic projections by the Institute for Health Metrics and Evaluation (IHME). While other asymmetric distributions too allow for the modeling of asymmetry, the ability to separately model the above three stages of an epidemic’s progression is a distinct feature of the model proposed. The latter avoids unrealistic projections of an epidemic’s right-tail in the absence of right tail data, which is an artifact of any fatality rate model where a left-tail fit determines its right-tail behavior.

Reaction-diffusion systems with cross-diffusion terms in addition to, or instead of, the usual self-diffusion demonstrate interesting features which motivate their further study. The present work is aimed at designing a toy reaction-cross-diffusion model with exact solutions in the form of propagating fronts. We propose a minimal model of this kind which involves two species linked by cross-diffusion, one of which governed by a linear equation and the other having a polynomial kinetic term. We classify the resulting exact propagating front solutions. Some of them have some features of the Fisher-KPP fronts and some features of the ZFK-Nagumo fronts.

A topic in the Sina-Microblog consists of multiple Weibos sharing a specific set of key words. Therefore, a reader of a Weibo may be susceptible to other Weibos of the same topic, and hence may undergo multiple transitions between the susceptible and the exposed states before eventually becoming infectious. This complicates the reading dynamics that traditional epidemic susceptible-exposed-infectious compartmental models cannot capture, and requires a different setting than a single Weibo forwarding dynamics model. Here we formulate a topic reading dynamics model; introduce some summative indices characterizing the reading “outbreak” potential; derive analytic formulae to calculate these indices; and examine the sensitivity of these indices and the ability of prediction on model parameters and on sampling frequencies. We conduct some numerical experiments based on historical data of popular reading topics in the Chinese Sina-Microblog. In our experiments, different sampling frequencies (4 h, 8 h or 12 h) nearcasting the turning points are feasible, in particular, a good nearcasting prediction for the accumulated R-users are 72 h with the sampling frequencies of every 4 h and every 12 h and 78 h with the sampling frequency of every 8 h.

The complex organization of gait variability, or fractal dynamics, theoretically represents the adaptive capacity of the locomotor system. Prior studies suggest that fractal dynamics are sensitive to various individual constraints (e.g., age, neurological disease) and task constraints (e.g., walking speed or novel gait tasks). The purpose of this study was to determine if physical activity levels represent an additional individual constraint during walking. Fifteen young and 15 older adults walked on a treadmill at their preferred walking speed and at half of their preferred speed. Detrended fluctuation analysis was used to estimate the statistical persistence of stride time variability. Volume of physical activity was determined using a wearable monitor for 3-7 days. Habitual physical activity levels did not appear to have an effect on the fractal nature of stride-to-stride fluctuations in young adults. However, the least active older adults displayed higher scaling exponents compared to the more active older adults during slow walking. That is, less active older adults responded to the slow walking task by increasing fractal scaling, suggesting this task is more challenging and complex. These findings suggest that lower levels of habitual physical activity may represent an additional individual constraint in older but not young adults. When age and physical activity constraints are combined with a challenging slow walking task, the locomotor system of older adults may be more highly taxed, ultimately manifesting as stronger statistical persistence.

We show that, among all smooth one-dimensional maps conjugated to an N-ary shift (a Bernoulli shift of N symbols), Chebyshev maps are distinguished in the sense that they have least higher-order correlations. We generalise our consideration and study a family of shifted Chebyshev maps, presenting analytic results for two-point and higher-order correlation functions. We also review results for the eigenvalues and eigenfunctions of the Perron-Frobenius operator of Nth order Chebyshev maps and their shifted generalisations. The spectrum is degenerate for odd N. Finally, we consider coupled map lattices (CMLs) of shifted Chebyshev maps and numerically investigate zeros of the temporal and spatial nearest-neighbour correlations, which are of interest in chaotically quantized field theories.

This comment shows that data regarding cumulative confirmed cases from the coronavirus COVID-19 disease outbreak, in the period December 31, 2019–June 29, 2020 of some countries reported by the European Centre for Disease Prevention and Control, can be adjusted by the exact solution of the Kermack – McKendrick approximation of the SIR epidemiological model.

In this paper, we study Hyers-Ulam stability and generalized Hyers-Ulam stability of linear equations with weighted Caputo-Fabrizio fractional derivative. We establish existence and uniqueness of solutions for nonlinear equations using Schaefer’s fixed point theorem. In addition, we present a generalized Hyers-Ulam stability result via the Gronwall inequality. Finally, two examples are given to illustrate our main results.