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In this paper, a novel variational Bayesian (VB) based adaptive Kalman filter (VBAKF) for linear Gaussian state-space models with inaccurate process and measurement noise covariance matrices is proposed. By choosing inverse Wishart priors, the state together with the predicted error and measurement noise covariance matrices are inferred based on the VB approach. Simulation results for a target tracking example illustrate that the proposed VBAKF has better robustness to resist the uncertainties of process and measurement noise covariance matrices than existing state-of-the-art filters.

Sub-nanometric Pd clusters on porous nanorods of CeO2 (PN-CeO2) with a high Pd dispersion of 73.6% exhibit the highest catalytic activity and best chemoselectivity for hydrogenation of nitroarenes to date. For hydrogenation of 4-nitrophenol, the catalysts yield a TOF of ∼44059 h(-1) and a chemoselectivity to 4-aminophenol of >99.9%. The superior catalytic performance can be attributed to a cooperative effect between the highly dispersed sub-nanometric Pd clusters for hydrogen activation and unique surface sites of PN-CeO2 with a high concentration of oxygen vacancy for an energetically and geometrically preferential adsorption of nitroarenes via nitro group. The high concentration of surface defects of PN-CeO2 and large Pd dispersion contribute to the enhanced catalytic activity for the hydrogenation reactions. The high chemoselectivity is mainly governed by the high Pd dispersion on the support. The catalysts also deliver high catalytic activity and selectivity for nitroaromatics with various reducible substituents into the corresponding aminoarenes.

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A novel robust Student's t based Kalman filter.Abstract-A novel robust Student's t based Kalman filter is proposed by using the variational Bayesian approach, which provides a Gaussian approximation to the posterior distribution. Simulation results for a manoeuvring target tracking example illustrate that the proposed filter has smaller root mean square error and bias than existing filters.