Angle-dependent electron-electron correlation in the single ionization of H2 in strong laser fields.

The one-photon ionization and tunneling ionization of H2 exposed to strong XUV and infrared laser pulses are studied by numerically simulating the four-dimensional time-dependent Schrödinger equation, which includes two-electron dynamics for arbitrary angle between the molecular axis and the laser polarization direction. In the one-photon single ionization of H2, one electron escapes fast and the other bound electron is not disturbed but remains in coherent superposition of two electronic states of [Formula: see text]. In another case, under the irradiation of strong infrared laser pulses, one electron tunnels through the laser-dressed Coulomb barrier, and the other bound electron has enough time to adapt to the potential of [Formula: see text] and thus is prone to transfer to the ground electronic state of [Formula: see text]. In the intermediate regime, between the one photon and tunneling regimes, this electron-electron correlation depends strongly on the laser frequency, laser intensity and on the angle between laser polarization and the molecular axis.

Spatial analysis of macro-level bicycle crashes using the class of conditional autoregressive models.

The objective of this study was to investigate the relationship between bicycle crash frequency and their contributing factors at the census block group level in Florida, USA. Crashes aggregated over the census block groups tend to be clustered (i.e., spatially dependent) rather than randomly distributed. To account for the effect of spatial dependence across the census block groups, the class of conditional autoregressive (CAR) models were employed within the hierarchical Bayesian framework. Based on four years (2011-2014) of crash data, total and fatal-and-severe injury bicycle crash frequencies were modeled as a function of a large number of variables representing demographic and socio-economic characteristics, roadway infrastructure and traffic characteristics, and bicycle activity characteristics. This study explored and compared the performance of two CAR models, namely the Besag's model and the Leroux's model, in crash prediction. The Besag's models, which differ from the Leroux's models by the structure of how spatial autocorrelation are specified in the models, were found to fit the data better. A 95% Bayesian credible interval was selected to identify the variables that had credible impact on bicycle crashes. A total of 21 variables were found to be credible in the total crash model, while 18 variables were found to be credible in the fatal-and-severe injury crash model. Population, daily vehicle miles traveled, age cohorts, household automobile ownership, density of urban roads by functional class, bicycle trip miles, and bicycle trip intensity had positive effects in both the total and fatal-and-severe crash models. Educational attainment variables, truck percentage, and density of rural roads by functional class were found to be negatively associated with both total and fatal-and-severe bicycle crash frequencies.