Discontinuous and continuous transitions of collective behaviors in living systems

Living systems are full of astonishing diversity and complexity of life.Despite differences in the length scales and cognitive abilities of these systems,collective motion of large groups of individuals can emerge.It is of great importance to seek for the fundamental principles of collective motion,such as phase transitions and their natures.Via an eigen microstate approach,we have found a discontinuous transition of density and a continuous transition of velocity in the Vicsek models of collective motion,which are identified by the finite-size scaling form of order-parameter.At strong noise,living systems behave like gas.With the decrease of noise,the interactions between the particles of a living system become stronger and make them come closer.The living system experiences then a discontinuous gas-liquid like transition of density.The even stronger interactions at smaller noise make the velocity directions of the particles become ordered and there is a continuous phase transition of collective motion in addition.

Occurrence Regularity and Control Measures of Powdery Mildew in Strawberry

Powdery mildew is a major disease of strawberry.It harms both plants and fruits,seriously affecting the quality and yield of strawberry.This article briefly introduces the harms and occurrence regularity of powdery mildew in strawberry and summarizes the corresponding control measures,with a view to providing a certain scientific reference for the prevention and treatment of the disease.

Universality class of machine learning for critical phenomena

Herein,percolation phase transitions on a two-dimensional lattice were studied using machine learning techniques.Results reveal that different phase transitions belonging to the same universality class can be identified using the same neural networks(NNs),whereas phase transitions of different universality classes require different NNs.Based on this finding,we proposed the universality class of machine learning for critical phenomena.Furthermore,we investigated and discussed the NNs of different universality classes.Our research contributes to machine learning by relating the NNs with the universality class.

Eigen microstates and their evolutions in complex systems

Emergence refers to the existence or formation of collective behaviors in complex systems.Here,we develop a theoretical framework based on the eigen microstate theory to analyze the emerging phenomena and dynamic evolution of complex system.In this framework,the statistical ensemble composed of M microstates of a complex system with N agents is defined by the normalized N×M matrix A,whose columns represent microstates and order of row is consist with the time.The ensemble matrix A can be decomposed as■,where r=min(N,M),eigenvalueσIbehaves as the probability amplitude of the eigen microstate U_I so that■and U_I evolves following V_I.In a disorder complex system,there is no dominant eigenvalue and eigen microstate.When a probability amplitudeσIbecomes finite in the thermodynamic limit,there is a condensation of the eigen microstate UIin analogy to the Bose–Einstein condensation of Bose gases.This indicates the emergence of U_I and a phase transition in complex system.Our framework has been applied successfully to equilibrium threedimensional Ising model,climate system and stock markets.We anticipate that our eigen microstate method can be used to study non-equilibrium complex systems with unknown orderparameters,such as phase transitions of collective motion and tipping points in climate systems and ecosystems.

Network approaches to climate science

Climate and physics are closely related.The governing equations for both the atmosphere and ocean are the Navier-Stokes equations,which describe and quantify the physics of fluids.However,the atmosphere and ocean are very complex due to the interaction of many processes,not necessarily physical,and due to the need to model processes that are smaller than the grid resolution.In spite of this complexity,physicists have successfully addressed a wide swath of it using advanced statistical physics methods and techniques.