Humanity has just crossed a major landmark in its history with the majority of people now living in cities. Cities have long been known to be society's predominant engine of innovation and wealth creation, yet they are also its main source of crime, pollution, and disease. The inexorable trend toward urbanization worldwide presents an urgent challenge for developing a predictive, quantitative theory of urban organization and sustainable development. Here we present empirical evidence indicating that the processes relating urbanization to economic development and knowledge creation are very general, being shared by all cities belonging to the same urban system and sustained across different nations and times. Many diverse properties of cities from patent production and personal income to electrical cable length are shown to be power law functions of population size with scaling exponents, ␤, that fall into distinct universality classes. Quantities reflecting wealth creation and innovation have ␤ Ϸ1.2 >1 (increasing returns), whereas those accounting for infrastructure display ␤ Ϸ0.8 <1 (economies of scale). We predict that the pace of social life in the city increases with population size, in quantitative agreement with data, and we discuss how cities are similar to, and differ from, biological organisms, for which ␤<1. Finally, we explore possible consequences of these scaling relations by deriving growth equations, which quantify the dramatic difference between growth fueled by innovation versus that driven by economies of scale. This difference suggests that, as population grows, major innovation cycles must be generated at a continually accelerating rate to sustain growth and avoid stagnation or collapse. population ͉ sustainability ͉ urban studies ͉ increasing returns ͉ economics of scale H umanity has just crossed a major landmark in its history with the majority of people now living in cities (1, 2). The present worldwide trend toward urbanization is intimately related to economic development and to profound changes in social organization, land use, and patterns of human behavior (1, 2). The demographic scale of these changes is unprecedented (2, 3) and will lead to important but as of yet poorly understood impacts on the global environment. In 2000, Ͼ70% of the population in developed countries lived in cities compared with Ϸ40% in developing countries. Cities occupied a mere 0.3% of the total land area but Ϸ3% of arable land. By 2030, the urban population of developing countries is expected to more than double to Ϸ4 billion, with an estimated 3-fold increase in occupancy of land area (3), whereas in developed countries it may still increase by as much as 20%. Paralleling this global urban expansion, there is the necessity for a sustainability transition (4-6) toward a stable total human population, together with a rise in living standards and the establishment of long-term balances between human development needs and the planet's environmental limits (7). Thus, a major challenge worldwide (5, 6) is to un...

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We discuss why disasters occur more frequently and are more serious than expected according to a normal distribution. Moreover, we investigate the interaction networks responsible for the cascade-like spreading of disasters. Such causality networks allow one to estimate the development of disasters with time, to give hints about when to take certain actions, to assess the suitability of alternative measures of emergency management, and to anticipate their side effects. Finally, we identify other fields where network theory could help to improve disaster response management.

We study the effectiveness of recovery strategies for a dynamic model of failure spreading in networks. These strategies control the distribution of resources based on information about the current network state and network topology. In order to assess their success, we have performed a series of simulation experiments. The considered parameters of these experiments are the network topology, the response time delay and the overall disposition of resources. Our investigations are focused on the comparison of strategies for different scenarios and the determination of the most appropriate strategy. The importance of prompt response and the minimum sufficient quantity of resources are discussed as well.

In modern industrial plants, process units are strongly cross-linked with each other, and disturbances occurring in one unit potentially become plant-wide. This can lead to a flood of alarms at the supervisory control and data acquisition system, hiding the original fault causing the disturbance. Hence, one major aim in fault diagnosis is to backtrack the disturbance propagation path of the disturbance and to localize the root cause of the fault. Since detecting correlation in the data is not sufficient to describe the direction of the propagation path, cause-effect dependencies among process variables need to be detected. Process variables that show a strong causal impact on other variables in the process come into consideration as being the root cause. In this paper, different data-driven methods are proposed, compared and combined that can detect causal relationships in data while solely relying on process data. The information of causal dependencies is used for localization of the root cause of a fault. All proposed methods consist of a statistical part, which determines whether the disturbance traveling from one process variable to a second is significant, and a quantitative part, which calculates the causal information the first process variable has about the second. The methods are tested on simulated data from a chemical stirred-tank reactor and on a laboratory plant.