Tsunami wave runup on the clustered islands using boussinesq-type model

This paper describes the implementation of the Boussinesq-type model and extends its application to the tsunami wave runup on the clustered islands (multiple adjacent conical islands), in turn, an extensively validated two-dimensional Boussinesq-type model is employed to examine the interaction between a propagating solitary wave and multiple idealised conical islands, with particular emphasis on a combination effect of two adjustable parameters for spacing interval/diameter ratio between the adjacent conical islands, S/D, and the rotating angle of the structural configuration,θ on maximum soliton runup heights. An extensive parameter study concerning the combination effect of alteringθ and S/D on the maximum soliton runup with the multi-conical islands is subsequently carried out and the distributions of the maximum runup heights on each conical island are obtained and compared for the twin-island cases. The worst case study is performed for each case in respect of the enhancement in the maximum wave runup heights by the multi-conical islands. It is found that the nonlinear wave diffraction, reflection and refraction play a significant role in varying the maximum soliton runup heights on multiconical islands. The comparatively large maximum soliton runups are generally predicted for the merged and bottom mounted clusteredislands. Furthermore, the joints of the clustered-merged islands are demonstrated to suffer the most of the tsunami wave attack. The conical islands that position in the shadow regions behind the surrounding islands are found to withstand relatively less extreme wave impact. Although, these numerical investigations are considerable simplifications of the multi conical islands, they give a critical insight into certain important hydrodynamic characteristics of the interaction between an extreme wave event and a group of clustered conical islands, and thus providing a useful engineering guidance for extreme wave mitigation and coastal development. Copyright © 2012 by the International Society of Offshore and Polar Engineers (ISOPE).

Internal representations of temporal statistics and feedback calibrate motor-sensory interval timing.

Humans have been shown to adapt to the temporal statistics of timing tasks so as to optimize the accuracy of their responses, in agreement with the predictions of Bayesian integration. This suggests that they build an internal representation of both the experimentally imposed distribution of time intervals (the prior) and of the error (the loss function). The responses of a Bayesian ideal observer depend crucially on these internal representations, which have only been previously studied for simple distributions. To study the nature of these representations we asked subjects to reproduce time intervals drawn from underlying temporal distributions of varying complexity, from uniform to highly skewed or bimodal while also varying the error mapping that determined the performance feedback. Interval reproduction times were affected by both the distribution and feedback, in good agreement with a performance-optimizing Bayesian observer and actor model. Bayesian model comparison highlighted that subjects were integrating the provided feedback and represented the experimental distribution with a smoothed approximation. A nonparametric reconstruction of the subjective priors from the data shows that they are generally in agreement with the true distributions up to third-order moments, but with systematically heavier tails. In particular, higher-order statistical features (kurtosis, multimodality) seem much harder to acquire. Our findings suggest that humans have only minor constraints on learning lower-order statistical properties of unimodal (including peaked and skewed) distributions of time intervals under the guidance of corrective feedback, and that their behavior is well explained by Bayesian decision theory.