Shakespeare the Elizabethan

Paper by Virgil K. Whitake

Spherical collapse model in agegraphic dark energy cosmologies

Under the commonly used spherical collapse model, we study how dark energy affects the growth of large scale structures of the Universe in the context of agegraphic dark energy models. The dynamics of the spherical collapse of dark matter halos in nonlinear regimes is determined by the properties of the dark energy model. We show that the main parameters of the spherical collapse model are directly affected by the evolution of dark energy in the agegraphic dark energy models. We compute the spherical collapse quantities for different values of agegraphic model parameter $\alpha$ in two different scenarios: first, when dark energy does not exhibit fluctuations on cluster scales, and second, when dark energy inside the overdense region collapses similar to dark matter. Using the Sheth-Tormen and Reed mass functions, we investigate the abundance of dark matter halos in the framework of agegraphic dark energy cosmologies. The model parameter $\alpha$ is a crucial parameter in order to count the abundance of dark matter halos. Specifically, the present analysis suggests that the agegraphic dark energy model with bigger (smaller) value of $\alpha$ predicts less (more) virialized halos with respect to that of $\Lambda$CDM cosmology. We also show that in agegraphic dark energy models, the number of halos strongly depends on clustered or uniformed distributions of dark energy.Comment: 14 pages, 7 figures. Accepted in Physical Review

Design of thrust vectoring exhaust nozzles for real-time applications using neural networks

Thrust vectoring continues to be an important issue in military aircraft system designs. A recently developed concept of vectoring aircraft thrust makes use of flexible exhaust nozzles. Subtle modifications in the nozzle wall contours produce a non-uniform flow field containing a complex pattern of shock and expansion waves. The end result, due to the asymmetric velocity and pressure distributions, is vectored thrust. Specification of the nozzle contours required for a desired thrust vector angle (an inverse design problem) has been achieved with genetic algorithms. This approach is computationally intensive and prevents the nozzles from being designed in real-time, which is necessary for an operational aircraft system. An investigation was conducted into using genetic algorithms to train a neural network in an attempt to obtain, in real-time, two-dimensional nozzle contours. Results show that genetic algorithm trained neural networks provide a viable, real-time alternative for designing thrust vectoring nozzles contours. Thrust vector angles up to 20 deg were obtained within an average error of 0.0914 deg. The error surfaces encountered were highly degenerate and thus the robustness of genetic algorithms was well suited for minimizing global errors

Capturing the time-varying drivers of an epidemic using stochastic dynamical systems

Epidemics are often modelled using non-linear dynamical systems observed through partial and noisy data. In this paper, we consider stochastic extensions in order to capture unknown influences (changing behaviors, public interventions, seasonal effects etc). These models assign diffusion processes to the time-varying parameters, and our inferential procedure is based on a suitably adjusted adaptive particle MCMC algorithm. The performance of the proposed computational methods is validated on simulated data and the adopted model is applied to the 2009 H1N1 pandemic in England. In addition to estimating the effective contact rate trajectories, the methodology is applied in real time to provide evidence in related public health decisions. Diffusion driven SEIR-type models with age structure are also introduced.Comment: 21 pages, 5 figure

Time-dependent photoionization of azulene: Competition between ionization and relaxation in highly excited states

Pump-probe photoionization has been used to map the relaxation processes taking place from highly vibrationally excited levels of the S2 state of azulene, populated directly or via internal conversion from the S4 state. Photoelectron spectra obtained by 1+2’ two-color time-resolved photoelectron imaging are invariant (apart from in intensity) to the pump-probe time delay and to pump wavelength. This reveals a photoionization process which is driven by an unstable electronic state (e.g. doubly excited state) lying below the ionization potential. This state is postulated to be populated by a probe transition from S2 and to rapidly relax via an Auger like process onto highly vibrationally excited Rydberg states. This accounts for the time invariance of the photoelectron spectrum. The intensity of the photoelectron spectrum is proportional to the population in S2. An exponential energy gap law is used to describe the internal conversion rate from S2 to S0. The vibronic coupling strength is found to be larger than 60±5 μeV