Herewith we discuss a network model of the ferroptosis avascular and vascular tumor growth based on our previous proposed framework. Chiefly, ferroptosis should be viewed as a first order phase transition characterized by a supercritical Andronov Hop
f bifurcation, with the emergence of limit cycle. The increase of the population of the oxidized PUFA fragments, take as the control parameter, involves an inverse Feigenbaum, (a cascade of saddle foci Shilnikovs bifurcations) scenario, which results in the stabilization of the dynamics and in a decrease of complexity.
The COVID-19 pandemic has completely disrupted the operation of our societies. Its elusive transmission process, characterized by an unusually long incubation period, as well as a high contagion capacity, has forced many countries to take quarantine
and social isolation measures that conspire against the performance of national economies. This situation confronts decision makers in different countries with the alternative of reopening the economies, thus facing the unpredictable cost of a rebound of the infection. This work tries to offer an initial theoretical framework to handle this alternative.
The patterns of motion of mobile agents has received recently wide attention in the literature. There is a number of recent studies centered around the motion behavior of many agents ranging from albatrosses to human beings. Special attention has bee
n given to the covered distances statistical distributions. In some cases, due to the lack of accurate data about the motion of the agents it has been necessary to plan very clever experiments to obtain them. These experiments try to infer the statistical properties of the agents real motion from the observed positions in consecutive time intervals. The length of the time intervals is a random variable taking values from a previously known statistical distribution or from a distribution deduced from empirical data. The aim of this work is to demonstrate that for a Gaussian Random Walker it is, in general, impossible to recover the real motion patterns distribution from the stroboscopic observation of the agents. Moreover, it is also shown that the distances distribution strongly depends on the agents observation time intervals. These claims are sustained by numerical experiments.
The network of 5823 cities of Mexico with a population more than 5000 inhabitants is studied. Our analysis is focused to the spectral properties of the adjacency matrix, the small-world properties of the network, the distribution of the clustering co
efficients and the degree distribution of the vertices. The connection of these features with the spread of epidemics on this network is also discussed.
An empirical law for the rank-order behavior of journal impact factors is found. Using an extensive data base on impact factors including journals on Education, Agrosciences, Geosciences, Biosciences and Environ- mental, Chemical, Computer, Engineeri
ng, Material, Mathematical, Medical and Physical Sciences we have found extremely good fits out- performing other rank-order models. Some extensions to other areas of knowledge are discussed.
We calculate the mutual information function for each of the 24 chromosomes in the human genome. The same correlation pattern is observed regardless the individual functional features of each chromosome. Moreover, correlations of different scale leng
th are detected depicting a multifractal scenario. This fact suggest a unique mechanism of structural evolution. We propose that such a mechanism could be an expansion-modification dynamical system.
In this paper we develop a method to analyze the increase of complexity from classical Greek poetry to classical Latin poetry by mapping large samples of those poetry onto a symbolic time series. This mapping setup intends characterize regular succes
sion of rhythms, that is, the patterns of stressed and unstressed syllables in a verse. Using techniques from information theory, more precisely, certain Renyi entropy we show how the rhythmical patterns in Greek poetry evolve to more complex behavior in Latin poetry. Some interesting results are reported.
A new approach to the understanding of complex behavior of financial markets index using tools from thermodynamics and statistical physics is developed. Physical complexity, a magnitude rooted in Kolmogorov-Chaitin theory is applied to binary sequenc
es built up from real time series of financial markets indexes. The study is based on NASDAQ and Mexican IPC data. Different behaviors of this magnitude are shown when applied to the intervals of series placed before crashes and to intervals when no financial turbulence is observed. The connection between our results and The Efficient Market Hypothesis is discussed.
Based on a model first studied in [Physical Review A, vol. 43, 5240-5260,1991] properties of correlation function for expansion-modification systems are developed. The existence of several characteristic exponents is proved. The relationship of this
fact with long-range correlation in DNA is stablished. Comparison between theoretical exponents and those obtained from simulation and real sequences are also showed.
