No abstract

No abstract

Using the spontaneously synchronized clapping in a concert hall as a special case, we build a nonlinear emergent model to characterize the collective behaviors of the complex multi-agent systems. Based on this model, we develop an experimental platform for emergent computation, which only depends on the local interaction and reveals the uncertainty and diversity of emergent behaviors. By analyzing the data in the procedure of many hands clapping, we find that there exists an obvious critical region generated in the procedure from disorder to synchronization. Moreover, we propose a fundamental synchronous criterion as follows: If the coupling coefficients c 1 and c 2 satisfy the condition 0.02 c 2 0.965c 1 +0.018, then the clapping can realize synchronization.

Flx attenuated the motor deficits induced by 3-NP in a dose-dependent manner. In contrary to the high dose, Flx at the lower doses had a more remarkable effect against 3-NP insult, similar to acute ischemic stroke.

Recently, complex multiagent networks have received increasing attention from various fields of science and engineering. Synchronization is a typical collective behavior of complex multiagent networks that has extensively been investigated over the last decade. To reveal the dynamical mechanism of synchronization in complex multiagent networks, a simple complex multiagent network with local information is then further investigated. Based on a suitable model, we analyze the inherent key factors in the emerging clapping synchronization. In particular, we explore two challenging fundamental questions: 1) How does the number of informed agents (or backbones) affect the emerging clapping synchronization? 2) How does the distribution of informed agents (or backbones) affect the emerging clapping synchronization? Our results indicate that the emerging clapping synchronization has a great diversity of routes, uncertainty, and adaptability. Moreover, our model and approach provide a possible route for analyzing the other collective behaviors of complex multiagent networks with local information via local control.