We use molecular dynamics simulations to study the diffusion of water inside deformed carbon nanotubes with different degrees of deformation at 300 K. It is found that the number of hydrogen bonds that water forms depends on nanotube topology, leading to an enhancement or suppression of water diffusion. The simulation results reveal that more realistic nanotubes should be considered to understand the confined water diffusion behavior, at least for the narrowest nanotubes, when the interaction between water molecules and carbon atoms are more relevant.

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In this article, we investigate, through molecular dynamics simulations, the diffusion behavior of the TIP4P/2005 water confined in pristine and deformed carbon nanotubes (armchair and zigzag). To analyze different diffusive mechanisms, the water temperature was varied as 210 ≤ T ≤ 380 K. The results of our simulations reveal that water presents a non-Arrhenius to Arrhenius diffusion crossover. The confinement shifts the diffusion transition to higher temperatures when compared with the bulk system. In addition, for narrower nanotubes, water diffuses in a single line, which leads to its mobility independent of the activation energy.

This paper explores the use of a particle filter—a data assimilation method—to incorporate real-time data into an agent-based model. We apply the method to a simulation of real pedestrians moving through the concourse of Grand Central Terminal in New York City (USA).  The results show that the particle filter does not perform well due to (i) the unpredictable behaviour of some pedestrians and (ii) because the filter does not optimise the categorical agent parameters that are characteristic of this type of model. This problem only arises because the experiments use real-world pedestrian movement data, rather than simulated, hypothetical data, as is more common. We point to a potential solution that involves resampling some of the variables in a particle, such as the locations of the agents in space, but keeps other variables such as the agents’ choice of destination. This research illustrates the importance of including real-world data and provides a proof of concept for the application of an improved particle filter to an agent-based model.  The obstacles and solutions discussed have important implications for future work that is focused on building large-scale real-time agent-based models.

This paper explores the use of a particle filter—a data assimilation method—to incorporate real-time data into an agent-based model. We apply the method to a simulation of real pedestrians moving through the concourse of Grand Central Terminal in New York City (USA).  The results show that the particle filter does not perform well due to (i) the unpredictable behaviour of some pedestrians and (ii) because the filter does not optimise the categorical agent parameters that are characteristic of this type of model. This problem only arises because the experiments use real-world pedestrian movement data, rather than simulated, hypothetical data, as is more common. We point to a potential solution that involves resampling some of the variables in a particle, such as the locations of the agents in space, but keeps other variables such as the agents’ choice of destination. This research illustrates the importance of including real-world data and provides a proof of concept for the application of an improved particle filter to an agent-based model.  The obstacles and solutions discussed have important implications for future work that is focused on building large-scale real-time agent-based models.