EPJ E publishes papers describing advances in the understanding of physical aspects of Soft Matter and Biological Systems. The journal includes reports of experimental, computational and theoretical studies and appeals to the broad interdisciplinary communities including physics, chemistry, biology, mathematics and materials science. Unique features of EPJ E: Tips and tricks short papers focused on a novel methodological approach that enables new science Colloquia papers reviewing new directions in Research Prestigious international board of editors Highly competent and fast editorial handling Global contributions and global readership Commonly used title abbreviations: Eur. Phys. J. E, Eur.Phys.J.E, EPJE, EPJ E

The European Physical Journal D

The possibility to analyze everyday monetary transactions is limited by the scarcity of available data, as this kind of information is usually considered highly sensitive. Present econophysics models are usually employed on presumed random networks of interacting agents, and only some macroscopic properties (e.g. the resulting wealth distribution) are compared to real-world data. In this paper, we analyze Bitcoin, which is a novel digital currency system, where the complete list of transactions is publicly available. Using this dataset, we reconstruct the network of transactions and extract the time and amount of each payment. We analyze the structure of the transaction network by measuring network characteristics over time, such as the degree distribution, degree correlations and clustering. We find that linear preferential attachment drives the growth of the network. We also study the dynamics taking place on the transaction network, i.e. the flow of money. We measure temporal patterns and the wealth accumulation. Investigating the microscopic statistics of money movement, we find that sublinear preferential attachment governs the evolution of the wealth distribution. We report a scaling law between the degree and wealth associated to individual nodes.

/pdf/do-the-rich-get-richer-an-empirical-analysis-of-the-bitcoin-4a5a1x2onm.pdf

Do the rich get richer? An empirical analysis of the Bitcoin transaction network.

Нефть и нефтепродукты вызывают практически полную депрессию функциональной активности флоры и фауны. Ингибируется жизнедеятельность большинства микроорганизмов, включая их ферментативную активность. Управлениепроцессами биодеградации нефти должно бытьнаправлено, прежде всего, на активизацию микробных сообществ, создание оптимальных условий их существования [1]. Отмечается большаянеоднородность распределения нефтяных компонентов в почвах разных участков нефтепромыслов, что зависит от физических и химическихсвойств конкретных почвенных разностей, качества и состава поступившей нефти [2]. В результате этого условия самоочищения окружающей среды от токсичных органических веществ техногенного происхождения в ландшафтных зонах и областях России различны [3].Исследование нефтезагрязненных почв проводилось на базе ботанического сада ГОУ ОГУ и влаборатории химикобиологического факультетаОГУ. В лабораторных и полевых условиях изучалось изменение биологической активности почв,загрязненных нефтью, и почв на различных стадиях рекультивации микробиологическим препаратом «Ленойл». При биоремедиации был использован биопрепарат нефтедеструктор «Ленойл», основанный на природном консорциуме микроорганизмов Bacillus brevis и Arthrobacter species, которые врезультате своей жизнедеятельности активно утилизируют углеводороды нефти и нефтепродуктов.В диагностических целях был выбран почвенный фермент – каталаза, так как ферментативнаяактивность почв – это один из показателей ее биологической активности, характеризующий потенциальную способность экосистемы сохранять гомеостаз.Активность каталазы резко снижается призагрязнении почвы нефтью, причем степень угнетения активности фермента зависела от концентрации поллютанта. В нашем случае концентрациясоставляла от 1%, до 15 % с шагом в 5%. Загрязнение нефтью в малых и средних концентрации 1% и

Сведения об авторах: Шамраев Александр Владимирович, кандидат биологических наук, доцент кафедры общей биологии Оренбургского государственного университета Гончарова Ольга Николаевна, соискатель кафедры экологии, общей биологии и МПБД

Increasingly, a huge amount of statistics have been gathered which clearly indicates that income and wealth distributions in various countries or societies follow a robust pattern, close to the Gibbs distribution of energy in an ideal gas in equilibrium. However, it also deviates in the low income and more significantly for the high income ranges. Application of physics models provides illuminating ideas and understanding, complementing the observations.

pdf/kinetic-exchange-models-for-income-and-wealth-distributions-sv0pe9ndaj.pdf

Kinetic Exchange Models for Income and Wealth Distributions

Kinetic exchange models for income and wealth distributions

We analyse the data from the recently published lists of the richest Chinese from the year 2003 to 2005. The results confirm that in these years the wealth is distributed according to a power law with exponents between 1.758 and 2.285 in the high end. The power distribution is found to be quite robust although the persons in the list change drastically and the wealth increases rapidly. The relation between the wealth and the absolute change of wealth rejects the notion that the wealth evolution is a multiplicative stochastic process.

https://iopscience.iop.org/article/10.1088/0256-307X/24/8/076/pdf

Power-Law Tail in the Chinese Wealth Distribution

With income data from Chinese household income projects in 1998–2002, we study the functional form of Chinese income distribution. The fitting results suggest a log-normal distribution plus a power-law tail. This distributional form has changed a lot from its appearance in the early stage of China's reform and turns out to be consistent with that of some complete market economies. The uncertainty and diversity of income growth rate aroused by marketing reform are the main causes of current Chinese income distribution.

https://iopscience.iop.org/article/10.1088/0256-307X/27/7/078901/pdf

Evidence of Scaling in Chinese Income Distribution

We systematically study the size dependency of income distributions, i.e. income distribution versus the population of a country. Using the generalized Lotka—Volterra model to fit the empirical income data for 1996–2007 in the U.S.A., we find an important parameter λ that can scale with a β power of the size (population) of the U.S.A. in that year. We point out that the size dependency of income distributions, which is a very important property but seldom addressed in previous studies, has two non-trivial implications: (1) the allometric growth pattern, i.e. the power-law relationship between population and GDP in different years, can be mathematically derived from the size-dependent income distributions and also supported by the empirical data; (2) the connection with the anomalous scaling for the probability density function in critical phenomena, since the re-scaled form of the income distributions has asymptotically exactly the same mathematical expression for the limit distribution of the sum of many correlated random variables.

Size Dependency of Income Distribution and Its Implications

Increasing gap in wealth distribution is among the key issues that have been discussed worldwide in recent years. In this paper, we use the money transfer model to explain the formation of wealth distribution, by imposing two types of debt constraints, and the analytic function of wealth distribution is derived by adopting Boltzmann statistics. With a limit of individual debt, it is shown that the stationary distribution of wealth follows the exponential law, which is verified by many empirical studies. While the limit is imposed on the total amount of bank loan, the stationary distribution becomes an asymmetric Laplace one. Furthermore, an excellent agreement is found between these analytical probability density functions and numerical results by simulation at the steady state.

/pdf/analysis-of-wealth-inequality-with-a-random-money-transfer-25vjl5fwsb.pdf

Analysis of wealth inequality with a random money transfer model

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Papers

Power-Law Tail in the Chinese Wealth Distribution

Evidence of Scaling in Chinese Income Distribution

Size Dependency of Income Distribution and Its Implications

Analysis of wealth inequality with a random money transfer model