Non-rigid image registration to reduce beam-induced blurring of cryo-electron microscopy images

Microscopic imaging and technolog

Quantifying Spatiotemporal Chaos in Rayleigh-B\'enard Convection

Using large-scale parallel numerical simulations we explore spatiotemporal chaos in Rayleigh-B\'enard convection in a cylindrical domain with experimentally relevant boundary conditions. We use the variation of the spectrum of Lyapunov exponents and the leading order Lyapunov vector with system parameters to quantify states of high-dimensional chaos in fluid convection. We explore the relationship between the time dynamics of the spectrum of Lyapunov exponents and the pattern dynamics. For chaotic dynamics we find that all of the Lyapunov exponents are positively correlated with the leading order Lyapunov exponent and we quantify the details of their response to the dynamics of defects. The leading order Lyapunov vector is used to identify topological features of the fluid patterns that contribute significantly to the chaotic dynamics. Our results show a transition from boundary dominated dynamics to bulk dominated dynamics as the system size is increased. The spectrum of Lyapunov exponents is used to compute the variation of the fractal dimension with system parameters to quantify how the underlying high-dimensional strange attractor accommodates a range of different chaotic dynamics

Prediction of Water Activity Coefficient in TEG-Water System Using Diffusion Neural Network (DNN)

Accurate determination of activity coefficients of water in a binary triethylene glycol (TEG)-water system, is of prime concern in designing the natural gas dehydration process. In this work, a hybrid model (a combination of information diffusion theory and neural network) and a so-called diffusion neural network (DNN) have been developed for the prediction of activity coefficients of a binary TEG-water system. Owing to the insufficient experimental data available in the literature for binary mixtures, and in particular for infinite dilution, we have employed the information diffusion technique as a tool in extrapolating data points from the original data. By means of this technique, a new dataset has been trained and optimized for the DNN model with more nodes in the input and the output layers. The result of this study reveals that DNN model is superior to the conventional neural nets in predicting the activity coefficient of water in the range of temperature (293–387.6 K) and mole fractions with mean absolute error of 0.31 % (MAE = 0.31 %), and high correlation coefficient of 0.999 (r = 0.999). Furthermore, the results of this work using DNN have also been compared with Parrish’s correlation. The findings of this work demonstrate that the DNN model exhibits better results over Parrish’s correlation in predicting the activity coefficients of water in a binary triethylene glycol-water system with a mean absolute error of 5.03 percent (MAE = 5.03 %)

LOCV calculations for polarized liquid 3He^3{He} with the spin-dependent correlation

We have used the lowest order constrained variational (LOCV) method to calculate some ground state properties of polarized liquid $^{3}He$ at zero temperature with the spin-dependent correlation function employing the Lennard-Jones and Aziz pair potentials. We have seen that the total energy of polarized liquid $^{3}He$ increases by increasing polarization. For all polarizations, it is shown that the total energy in the spin-dependent case is lower than the spin-independent case. We have seen that the difference between the energies of spin-dependent and spin-independent cases decreases by increasing polarization. We have shown that the main contribution of the potential energy comes from the spin-triplet state.Comment: 14 pages, 5 figures. Int. J. Mod. Phys. B (2008) in pres

Spin-to-Orbital Angular Momentum Conversion and Spin-Polarization Filtering in Electron Beams

We propose the design of a space-variant Wien filter for electron beams that induces a spin half-turn and converts the corresponding spin angular momentum variation into orbital angular momentum of the beam itself by exploiting a geometrical phase arising in the spin manipulation. When applied to a spatially coherent input spin-polarized electron beam, such a device can generate an electron vortex beam, carrying orbital angular momentum. When applied to an unpolarized input beam, the proposed device, in combination with a suitable diffraction element, can act as a very effective spin-polarization filter. The same approach can also be applied to neutron or atom beams.Comment: 9 pages, 5 figure