Potsdam Institute for Climate Impact Research

About: Potsdam Institute for Climate Impact Research is a facility organization based out in Potsdam, Germany. It is known for research contribution in the topics: Climate change & Global warming. The organization has 1519 authors who have published 5098 publications receiving 367023 citations.

A Constrained Evolutionary Computation Method for Detecting Controlling Regions of Cortical Networks

Abstract: Controlling regions in cortical networks, which serve as key nodes to control the dynamics of networks to a desired state, can be detected by minimizing the eigenratio R and the maximum imaginary part \sigma of an extended connection matrix. Until now, optimal selection of the set of controlling regions is still an open problem and this paper represents the first attempt to include two measures of controllability into one unified framework. The detection problem of controlling regions in cortical networks is converted into a constrained optimization problem (COP), where the objective function R is minimized and \sigma is regarded as a constraint. Then, the detection of controlling regions of a weighted and directed complex network (e.g., a cortical network of a cat), is thoroughly investigated. The controlling regions of cortical networks are successfully detected by means of an improved dynamic hybrid framework (IDyHF). Our experiments verify that the proposed IDyHF outperforms two recently developed evolutionary computation methods in constrained optimization field and some traditional methods in control theory as well as graph theory. Based on the IDyHF, the controlling regions are detected in a microscopic and macroscopic way. Our results unveil the dependence of controlling regions on the number of driver nodes l and the constraint r. The controlling regions are largely selected from the regions with a large in-degree and a small out-degree. When r=+ \infty, there exists a concave shape of the mean degrees of the driver nodes, i.e., the regions with a large degree are of great importance to the control of the networks when l is small and the regions with a small degree are helpful to control the networks when l increases. When r=0, the mean degrees of the driver nodes increase as a function of l. We find that controlling \sigma is becoming more important in controlling a cortical network with increasing l. The methods and results of detecting controlling regions in this paper would promote the coordination and information consensus of various kinds of real-world complex networks including transportation networks, genetic regulatory networks, and social networks, etc.

River flood risk and adaptation in Europe—assessment of the present status

Abstract: Flood disasters have had a devastating effect worldwide over the past century, both in terms of human suffering and material losses. The study of these events and development of more effective adaptation and mitigation policies has become a priority, both in Europe and other parts of the globe. This paper detects and presents the spatial distribution of river flood risks in Europe. The methodology we developed involves an assessment of three key risk components: exposure, vulnerability and hazard. A topography-based flood hazard map of Europe, identifying low-lying areas adjacent to rivers, is presented and used to identify risk, together with land-use data and damage-stage relationship for different land uses. The study covers river flood risk for the entire European continent. This methodology can be used to determine the level of future risk, using the estimations on Hazard, Exposure and Vulnerability from specific climate and economic development models. Annual average flood damage is estimated for European regions, in absolute monetary terms and in % of regional Gross Domestic Product (GDP). The results highlight regions where the threat to the economy from river flood hazard is of major concern.