To model liquid water correctly and to reproduce its structural, dynamic and thermodynamic properties warrants models that account accurately for electronic polarisation. We have previously demonstrated that polarisation can be represented by fluctuating multipole moments (derived by quantum chemical topology) predicted by multilayer perceptrons (MLPs) in response to the local structure of the cluster. Here we further develop this methodology of modeling polarisation enabling control of the balance between accuracy, in terms of errors in Coulomb energy and computing time. First, the predictive ability and speed of two additional machine learning methods, radial basis function neural networks (RBFNN) and Kriging, are assessed with respect to our previous MLP based polarisable water models, for water dimer, trimer, tetramer, pentamer and hexamer clusters. Compared to MLPs, we find that RBFNNs achieve a 14-26% decrease in median Coulomb energy error, with a factor 2.5-3 slowdown in speed, whilst Kriging achieves a 40-67% decrease in median energy error with a 6.5-8.5 factor slowdown in speed. Then, these compromises between accuracy and speed are improved upon through a simple multi-objective optimisation to identify Pareto-optimal combinations. Compared to the Kriging results, combinations are found that are no less accurate (at the 90th energy error percentile), yet are 58% faster for the dimer, and 26% faster for the pentamer.

Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. When attempting to determine how to respond optimally to a large-scale emergency, the ability to predict the consequences of certain courses of action in silico is of great utility. Agent-based simulations (ABSs) have become the de facto tool for this purpose, however they may be used and implemented in a variety of ways. This paper reviews existing implementations of ABSs for largescale emergency response, and presents a taxonomy classifying them by usage. Opportunities for improving ABS for large-scale emergency response are identified.

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Electrostatic effects play a large part in determining the properties of chemical systems. In addition, a treatment of the polarisation of the electron distribution is important for many systems, including solutions of monatomic ions. Typically employed methods for describing polarisable electrostatics use a number of approximations, including atom-centred point charges and polarisation methods that require iterative calculation on the fly. We present a method that treats charge transfer and polarisation on an equal footing. Atom-centred multipole moments describe the charge distribution of a chemical system. The variation of these multipole moments with the geometry of the surrounding atoms is captured by the machine learning method kriging. The interatomic electrostatic interaction can be computed using the resulting predicted multipole moments. This allows the treatment of both intra- and interatomic polarisation with the same method. The proposed method does not return explicit polarisabilities but instead, predicts the result of the polarisation process. An application of this new method to the sodium cation in a water environment is described. The performance of the method is assessed by comparison of its predictions of atomic multipole moments and atom-atom electrostatic interaction energies to exact results. The kriging models are able to predict the electrostatic interaction energy between the ion and all water atoms within 4 kJ mol(-1) for any of the external test set Na(+)(H2O)6 configurations.

a b s t r a c tDuring a major incident, the emergency services work together to ensure that those casualties who are critically injured are identified and transported to an appropriate hospital as fast as possible. If the incident is multi-site and resources are limited, the efficiency of this process is compromised as the finite resources must be shared among the multiple sites. In this paper, agent-based simulation is used to determine the allocation of resources for a two-site incident which minimizes the latest hospital arrival times for critically injured casualties. Further, how the optimal resource allocation depends on the distribution of casualties across the two sites is investigated. Such application supports the use of agentbased simulation as a tool to aid emergency response.