RESUMEN

Real-world attacks can be interpreted as the result of competitive interactions between networks, ranging from predator-prey networks to networks of countries under economic sanctions. Although the purpose of an attack is to damage a target network, it also curtails the ability of the attacker, which must choose the duration and magnitude of an attack to avoid negative impacts on its own functioning. Nevertheless, despite the large number of studies on interconnected networks, the consequences of initiating an attack have never been studied. Here, we address this issue by introducing a model of network competition where a resilient network is willing to partially weaken its own resilience in order to more severely damage a less resilient competitor. The attacking network can take over the competitor's nodes after their long inactivity. However, owing to a feedback mechanism the takeovers weaken the resilience of the attacking network. We define a conservation law that relates the feedback mechanism to the resilience dynamics for two competing networks. Within this formalism, we determine the cost and optimal duration of an attack, allowing a network to evaluate the risk of initiating hostilities.

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RESUMEN

The experts' judgement data on microbial degradation were used to develop the first general QSAR biodegradability model (Boethling and Sabljic, 1989) which is composed of a set of structural descriptors and a set of quantitative rules. Its evaluation and validation with experimental biodegradation data clearly show that the developed model gives a realistic and reliable account of structurebiodegradability relationship for organic chemicals. The same set of experts judgement data was used to develop structure-biodegradation rule by the application of an inductive machine learning method. An improved structure-biodegradation rule was derived from a larger training set of 160 chemicals, i.e. the combined experts' judgement and evaluated experimental biodegradation data. This rule has good predictive ability and discloses logical dependencies between structural features that have a strong influence on biodegradation of organic chemicals. Thus, the understanding of biodegradation processes will benefit from developed rule.