Rong Chen

Bio: Rong Chen is an academic researcher from Wuhan Institute of Technology. The author has contributed to research in topics: Photocatalysis & Raman spectroscopy. The author has an hindex of 65, co-authored 582 publications receiving 22888 citations. Previous affiliations of Rong Chen include University of Hong Kong & Stanford University.

Sequential Monte Carlo methods for dynamic systems

Abstract: We provide a general framework for using Monte Carlo methods in dynamic systems and discuss its wide applications. Under this framework, several currently available techniques are studied and generalized to accommodate more complex features. All of these methods are partial combinations of three ingredients: importance sampling and resampling, rejection sampling, and Markov chain iterations. We provide guidelines on how they should be used and under what circumstance each method is most suitable. Through the analysis of differences and connections, we consolidate these methods into a generic algorithm by combining desirable features. In addition, we propose a general use of Rao-Blackwellization to improve performance. Examples from econometrics and engineering are presented to demonstrate the importance of Rao–Blackwellization and to compare different Monte Carlo procedures.

Silver nanoparticles: partial oxidation and antibacterial activities.

Abstract: The physical and chemical properties of silver nanoparticles that are responsible for their antimicrobial activities have been studied with spherical silver nanoparticles (average diameter approximately 9 nm) synthesized by the borohydride reduction of Ag+ ions, in relation to their sensitivity to oxidation, activities towards silver-resistant bacteria, size-dependent activities, and dispersal in electrolytic solutions. Partially (surface) oxidized silver nanoparticles have antibacterial activities, but zero-valent nanoparticles do not. The levels of chemisorbed Ag+ that form on the particle's surface, as revealed by changes in the surface plasmon resonance absorption during oxidation and reduction, correlate well with the observed antibacterial activities. Silver nanoparticles, like Ag+ in the form of AgNO3 solution, are tolerated by the bacteria strains resistant to Ag+. The antibacterial activities of silver nanoparticles are related to their size, with the smaller particles having higher activities on the basis of equivalent silver mass content. The silver nanoparticles aggregate in media with a high electrolyte content, resulting in a loss of antibacterial activities. However, complexation with albumin can stabilize the silver nanoparticles against aggregation, leading to a retention of the antibacterial activities. Taken together, the results show that the antibacterial activities of silver nanoparticles are dependent on chemisorbed Ag+, which is readily formed owing to extreme sensitivity to oxygen. The antibacterial activities of silver nanoparticles are dependent on optimally displayed oxidized surfaces, which are present in well-dispersed suspensions.

Proteomic analysis of the mode of antibacterial action of silver nanoparticles

Abstract: Silver nanoparticles (nano-Ag) are potent and broad-spectrum antimicrobial agents. In this study, spherical nano-Ag (average diameter = 9.3 nm) particles were synthesized using a borohydride reduction method and the mode of their antibacterial action against E. coli was investigated by proteomic approaches (2-DE and MS identification), conducted in parallel to analyses involving solutions of Ag+ ions. The proteomic data revealed that a short exposure of E. coli cells to antibacterial concentrations of nano-Ag resulted in an accumulation of envelope protein precursors, indicative of the dissipation of proton motive force. Consistent with these proteomic findings, nano-Ag were shown to destabilize the outer membrane, collapse the plasma membrane potential and deplete the levels of intracellular ATP. The mode of action of nano-Ag was also found to be similar to that of Ag+ ions (e.g., Dibrov, P. et al, Antimicrob. Agents Chemother. 2002, 46, 2668−2670); however, the effective concentrations of nano-Ag and Ag...

Mixture Kalman filters

Abstract: In treating dynamic systems, sequential Monte Carlo methods use discrete samples to represent a complicated probability distribution and use rejection sampling, importance sampling and weighted resampling to complete the on-line ‘filtering’ task. We propose a special sequential Monte Carlo method, the mixture Kalman filter, which uses a random mixture of the Gaussian distributions to approximate a target distribution. It is designed for on-line estimation and prediction of conditional and partial conditional dynamic linear models, which are themselves a class of widely used non-linear systems and also serve to approximate many others. Compared with a few available filtering methods including Monte Carlo methods, the gain in efficiency that is provided by the mixture Kalman filter can be very substantial. Another contribution of the paper is the formulation of many non-linear systems into conditional or partial conditional linear form, to which the mixture Kalman filter can be applied. Examples in target tracking and digital communications are given to demonstrate the procedures proposed.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking

Abstract: Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-line as it arrives, both from the point of view of storage costs as well as for rapid adaptation to changing signal characteristics. In this paper, we review both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters. Particle filters are sequential Monte Carlo methods based on point mass (or "particle") representations of probability densities, which can be applied to any state-space model and which generalize the traditional Kalman filtering methods. Several variants of the particle filter such as SIR, ASIR, and RPF are introduced within a generic framework of the sequential importance sampling (SIS) algorithm. These are discussed and compared with the standard EKF through an illustrative example.