We analyze biased ensembles of trajectories for diffusive systems. In trajectories biased either by the total activity or the total current, we use fluctuating hydrodynamics to show that these systems exhibit phase transitions into "hyperuniform" states, where large-wavelength density fluctuations are strongly suppressed. We illustrate this behavior numerically for a system of hard particles in one dimension and we discuss how it appears in simple exclusion processes. We argue that these diffusive systems generically respond very strongly to any nonzero bias, so that homogeneous states with "normal" fluctuations (finite compressibility) exist only when the bias is very weak.

We analyze a one-dimensional model of hard particles, within ensembles of trajectories that are conditioned (or biased) to atypical values of the time-averaged dynamical activity. We analyze two phenomena that are associated with these large deviations of the activity: phase separation (at low activity) and the formation of hyperuniform states (at high activity). We consider a version of the model which operates at constant volume, and a version at constant pressure. In these nonequilibrium systems, differences arise between the two ensembles, because of the extra freedom available to the constant-pressure system, which can change its total density. We discuss the relationships between different ensembles, mechanical equilibrium, and the probability cost of rare density fluctuations.

A morpliological study of the Australian Senecio pinnatifoliusIS. kuitiis complex has resulted in a revised classification for this group. Australian plants historically included in S. lautus G.Forst. ex Willd. are treated as distinct at the level of species from this and other lautusoid taxa from New Zealand. Seven new species are described, predominantly from arid or semiarid regions. These are; S.

We study the effects of molecular ordering on charge transport at the mesoscale level in a layer of ≈9000 hexa-octyl-thio-triphenylene discotic mesogens with dimensions of ≈20 × 20 × 60 nm 3. Ordered (columnar) and disordered isotropic morphologies are obtained from a combination of atomistic and coarse-grained moleculardynamics simulations. Electronic structure codes are used to find charge hopping rates at the microscopic level. Energetic disorder is included through the Thole model. Kinetic Monte Carlo simulations then predict charge mobilities. We reproduce the large increase in mobility in going from an isotropic to a columnar morphology. To understand how these mobilities depend on the morphology and hopping rates, we employ graph theory to analyze charge trajectories by representing the film as a charge-transport network. This approach allows us to identify spatial correlations of molecule pairs with high transfer rates. These pairs must be linked to ensure good transport characteristics or may otherwise act as traps. Our analysis is straightforward to implement and will be a useful tool in linking materials to device performance, for example, to investigate the influence of local inhomogeneities in the current density. Our mobility-field curves show an increasing mobility with field, as would be expected for an organic semiconductor.

Morphological studies of Australian disciform Senecio (central florets of the capitulum bisexual and tubular, and marginal florets female and tubular) have resulted in the recognition of 18 new species, two new subspecies, and the resurrection of six names. The new species are: S.

A morphological study of the Australian annual daisy Senecio glossanthus (Sond.) Belcher has resulted in the recognition of three new species: S. halopbiliis l.Thomps., S. serratifonnis l.Thomps., and S. productiis l.Thomps. Two new subspecies are also described: S. prodiictus subsp. magniis l.Thomps. and S. serratifonnis subsp. stenophylhis l.Thomps. A further new species, S. condyhis I.TIiomps.. described here, has features in common with the S. glossanthus group but also has some affinity to the Australian S. lautus/S. pinnatifoUus complex. A key, distribution maps and illustrations of the new taxa are presented.

Kinetic Monte Carlo (KMC) is an important computational tool in theoretical physics and chemistry. In contrast to standard Monte Carlo, KMC permits the description of time dependent dynamical processes and is not restricted to systems in equilibrium. Compared to Molecular Dynamics, it allows simulations over significantly longer timescales. Recently KMC has been applied successfully in modelling of novel energy materials such as Lithium-ion batteries and organic/perovskite solar cells. Motivated by this, we consider general solid state systems which contain free, interacting particles which can hop between localised sites in the material. The KMC transition rates for those hops depend on the change in total potential energy of the system. For charged particles this requires the frequent calculation of electrostatic interactions, which is usually the bottleneck of the simulation. To avoid this issue and obtain results in reasonable times, many studies replace the long-range potential by a phenomenological short range approximation. This, however, leads to systematic errors and unphysical results. On the other hand standard electrostatic solvers such as Ewald summation or fast Poisson solvers are highly inefficient in the KMC setup or introduce uncontrollable systematic errors at high resolution.In this paper we describe a new variant of the Fast Multipole Method by Greengard and Rokhlin which overcomes this issue by dramatically reducing computational costs. We exploit the fact that each update in the transition rate calculation corresponds to a single particle move and changes the configuration only by a small amount. This allows us to construct an algorithm which scales linearly in the number of charges for each KMC step, something which had not been deemed to be possible before.We demonstrate the performance and parallel scalability of the method by implementing it in a performance portable software library, which was recently developed in our group. We describe the high-level Python interface of the code which makes it easy to adapt to specific use cases.

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