Pub Date : 2019-04-01DOI: 10.1142/9789813239609_0008
J. Rohling, J. Meijer
{"title":"Biorhythms and the brain","authors":"J. Rohling, J. Meijer","doi":"10.1142/9789813239609_0008","DOIUrl":"https://doi.org/10.1142/9789813239609_0008","url":null,"abstract":"","PeriodicalId":38342,"journal":{"name":"复杂系统与复杂性科学","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90511688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-26DOI: 10.1142/9789813239609_0012
R. Planqué, J. Hulshof
{"title":"Metabolic pathways and optimisation","authors":"R. Planqué, J. Hulshof","doi":"10.1142/9789813239609_0012","DOIUrl":"https://doi.org/10.1142/9789813239609_0012","url":null,"abstract":"","PeriodicalId":38342,"journal":{"name":"复杂系统与复杂性科学","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88400291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-26DOI: 10.1142/9789813239609_fmatter
M. Peletier, R. V. van Santen, E. Steur
{"title":"FRONT MATTER","authors":"M. Peletier, R. V. van Santen, E. Steur","doi":"10.1142/9789813239609_fmatter","DOIUrl":"https://doi.org/10.1142/9789813239609_fmatter","url":null,"abstract":"","PeriodicalId":38342,"journal":{"name":"复杂系统与复杂性科学","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73382261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-20DOI: 10.1142/9789813239609_0013
E. Cirillo, A. Muntean, R. V. Santen
Particle diffusion is modified by the presence of barriers. In cells macromolecules, behaving as obstacles, slow down the dynamics so that the meansquare displacement of molecules grows with time as a power law with exponent smaller than one. In different situations, such as grain and pedestrian dynamics, it can happen that an obstacle can accelerate the dynamics. In the framework of very basic models, we study the time needed by particles to cross a strip for different bulk dynamics and discuss the effect of obstacles. We find that in some regimes such a residence time is not monotonic with respect to the size and the position of the obstacles. We can then conclude that, even in very elementary systems where no interaction among particles is considered, obstacles can either slow down or accelerate the particle dynamics depending on their geometry and position.
{"title":"Particle-based modelling of flows through obstacles","authors":"E. Cirillo, A. Muntean, R. V. Santen","doi":"10.1142/9789813239609_0013","DOIUrl":"https://doi.org/10.1142/9789813239609_0013","url":null,"abstract":"Particle diffusion is modified by the presence of barriers. In cells macromolecules, behaving as obstacles, slow down the dynamics so that the meansquare displacement of molecules grows with time as a power law with exponent smaller than one. In different situations, such as grain and pedestrian dynamics, it can happen that an obstacle can accelerate the dynamics. In the framework of very basic models, we study the time needed by particles to cross a strip for different bulk dynamics and discuss the effect of obstacles. We find that in some regimes such a residence time is not monotonic with respect to the size and the position of the obstacles. We can then conclude that, even in very elementary systems where no interaction among particles is considered, obstacles can either slow down or accelerate the particle dynamics depending on their geometry and position.","PeriodicalId":38342,"journal":{"name":"复杂系统与复杂性科学","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80073555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-20DOI: 10.1142/9789813239609_0005
M. Peletier
In this chapter we give a short introduction to the concept of stochastic processes, evolution equations with random solutions. The best-known examples are random walks and stochastic differential equations, and we discuss examples of these and some of their properties, as well as methods for numerical simulation. We conclude with a brief introduction into metastability, the phenomenon that stochastic processes may have very different behaviour at different time scales.
{"title":"A primer on stochastic processes","authors":"M. Peletier","doi":"10.1142/9789813239609_0005","DOIUrl":"https://doi.org/10.1142/9789813239609_0005","url":null,"abstract":"In this chapter we give a short introduction to the concept of stochastic processes, evolution equations with random solutions. The best-known examples are random walks and stochastic differential equations, and we discuss examples of these and some of their properties, as well as methods for numerical simulation. We conclude with a brief introduction into metastability, the phenomenon that stochastic processes may have very different behaviour at different time scales.","PeriodicalId":38342,"journal":{"name":"复杂系统与复杂性科学","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89621900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-20DOI: 10.1142/9789813239609_0003
E. Steur, H. Nijmeijer
We give an introduction to the analysis of the dynamics of deterministic nonlinear systems from a systems and control point of view. In particular, we discuss the stabilizing or destabilizing effect of feedback interconnections in nonlinear dynamical systems. With the help of this machinery we explain two types of complex collective dynamics in networks of nonlinear systems.
{"title":"Complex dynamics of deterministic nonlinear systems","authors":"E. Steur, H. Nijmeijer","doi":"10.1142/9789813239609_0003","DOIUrl":"https://doi.org/10.1142/9789813239609_0003","url":null,"abstract":"We give an introduction to the analysis of the dynamics of deterministic nonlinear systems from a systems and control point of view. In particular, we discuss the stabilizing or destabilizing effect of feedback interconnections in nonlinear dynamical systems. With the help of this machinery we explain two types of complex collective dynamics in networks of nonlinear systems.","PeriodicalId":38342,"journal":{"name":"复杂系统与复杂性科学","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73285926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-20DOI: 10.1142/9789813239609_0006
R. Hofstad
In this chapter, we discuss complex networks as a prime example where the ideas from complexity theory can be successfully applied. Complex networks show emergent behavior in their connectivity, and they have intricate feedback mechanisms leading to non-linearities, particularly in settings where the network structure is highly heterogeneous. We draw motivation from real-world networks about the properties of such networks. We formulate random graph models for real-world networks and investigate the properties of these models, such as their degree structure, their connectivity and their small-world properties, as well as the behavior of stochastic processes on them. We focus on some models that have received the most attention in the literature, namely, the Erdos-Renyi random graph, inhomogeneous random graphs, the configuration model and preferential attachment models. We also discuss some of their extensions that have the potential to yield more realistic models for real-world networks. We close this chapter by speculating on applications of random graphs to the brain, which is arguably the most complex network that exists.
{"title":"Random graphs models for complex networks, and the brain","authors":"R. Hofstad","doi":"10.1142/9789813239609_0006","DOIUrl":"https://doi.org/10.1142/9789813239609_0006","url":null,"abstract":"In this chapter, we discuss complex networks as a prime example where the ideas from complexity theory can be successfully applied. Complex networks show emergent behavior in their connectivity, and they have intricate feedback mechanisms leading to non-linearities, particularly in settings where the network structure is highly heterogeneous. We draw motivation from real-world networks about the properties of such networks. We formulate random graph models for real-world networks and investigate the properties of these models, such as their degree structure, their connectivity and their small-world properties, as well as the behavior of stochastic processes on them. We focus on some models that have received the most attention in the literature, namely, the Erdos-Renyi random graph, inhomogeneous random graphs, the configuration model and preferential attachment models. We also discuss some of their extensions that have the potential to yield more realistic models for real-world networks. We close this chapter by speculating on applications of random graphs to the brain, which is arguably the most complex network that exists.","PeriodicalId":38342,"journal":{"name":"复杂系统与复杂性科学","volume":"76 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90937785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-20DOI: 10.1142/9789813239609_0009
B. Fitzgerald, R. V. Santen, JT Johan Padding
Collective motion can be observed in many systems at various length scales. Ranging from the interaction of microswimmers at the microscopic scale to the dynamics of people and flocking behaviours of birds at the macroscopic scale, the natural world is home to many examples of collective responses. The emergence of collective motion in systems has long fascinated the scientific community with the classical approach for their study based on experimental observation. However, the development of suitable computer algorithms has significantly supplemented and complemented these empirical studies while also motivating new research fields on collective behaviour. This chapter outlines methods for measuring collective motion and key algorithms for the simulation of collective responses in birds, fish, mammals and people.
{"title":"Modelling of collective motion","authors":"B. Fitzgerald, R. V. Santen, JT Johan Padding","doi":"10.1142/9789813239609_0009","DOIUrl":"https://doi.org/10.1142/9789813239609_0009","url":null,"abstract":"Collective motion can be observed in many systems at various length scales. Ranging from the interaction of microswimmers at the microscopic scale to the dynamics of people and flocking behaviours of birds at the macroscopic scale, the natural world is home to many examples of collective responses. The emergence of collective motion in systems has long fascinated the scientific community with the classical approach for their study based on experimental observation. However, the development of suitable computer algorithms has significantly supplemented and complemented these empirical studies while also motivating new research fields on collective behaviour. This chapter outlines methods for measuring collective motion and key algorithms for the simulation of collective responses in birds, fish, mammals and people.","PeriodicalId":38342,"journal":{"name":"复杂系统与复杂性科学","volume":"96 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80102064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-20DOI: 10.1142/9789813239609_0002
van P. Santen, A Rutger
Complementary to the previous chapter a tutorial introduction to complexity science is presented. The chapter focuses on the interrelation of complexity science concepts that vary from mathematics to physics and biology to the social sciences. An interesting aspect of complexity science is that its language as well as tools are of particular use to study problems that require a multidisciplinary approach.
{"title":"Disguises of complexity","authors":"van P. Santen, A Rutger","doi":"10.1142/9789813239609_0002","DOIUrl":"https://doi.org/10.1142/9789813239609_0002","url":null,"abstract":"Complementary to the previous chapter a tutorial introduction to complexity science is presented. The chapter focuses on the interrelation of complexity science concepts that vary from mathematics to physics and biology to the social sciences. An interesting aspect of complexity science is that its language as well as tools are of particular use to study problems that require a multidisciplinary approach.","PeriodicalId":38342,"journal":{"name":"复杂系统与复杂性科学","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89317153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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