Aerosol samples of PM 2.5 and PM 10 were collected in both summer and winter seasons from 2002 to 2003 synchronously at a traffic site, an industrial site, and a residential site in Beijing, which could basically be the representatives over Beijing. Twenty-three elements and 15 ions together with organic carbon and elemental carbon were analyzed systematically for characterization of Beijing aerosol. PM 2.5 was the major part of the inhalable particles (PM 10 ), as the ratios of PM 2.5 /PM 10 were 0.45-0.48 in summer and 0.52-0.73 in winter. SO 4 2À, NO 3 À , NH 4 + , organic matter, crustal matter, and element carbon were the six dominant species, which totally accounted for 85.8-97.7% of PM 2.5 . Secondary aerosol (SO 4 2À , NO 3 À , and NH 4 + ), road dust or/and long-range transported dust from outside Beijing, industry and motor vehicles emission, coal burning were the major contributors to the air-borne particulate pollution in Beijing. Overall, coal burning and the traffic exhausts, plus the dust from the long-range transport, could be the major sources of the aerosol pollution at Beijing. A relatively even spatial distribution of chemical species in PM 2.5 was found while in PM 10 a significant variation with the highest concentrations at the industrial site in summer and at the residential site in winter was observed. The concentrations of PM 10 , PM 2.5 as well as various chemical species were higher in winter than in summer. The contributions of mineral aerosol from outside Beijing were first estimated with a newly developed element tracer technique, which accounted for 79% and 37% of the total mineral in PM 10 and PM 2.5 in winter, and 19% and 20% in summer, respectively. During the dust storm period from 20 to 22 March, it reached up to 97% in TSP, 79% in PM 10 and 76% in PM 2.5 . This is the technique, firstly, developed for estimating the relative contributions of sources from inside and outside Beijing to the total mineral aerosol and it could provide the basic information in controlling the air-borne particulate pollution at Beijing. r

Catalases are key regulators of reactive oxygen species homeostasis in plant cells. However, the regulation of catalase activity is not well understood. In this study, we isolated an Arabidopsis thaliana mutant, no catalase activity1-3 (nca1-3) that is hypersensitive to many abiotic stress treatments. The mutated gene was identified by map-based cloning as NCA1, which encodes a protein containing an N-terminal RING-finger domain and a C-terminal tetratricopeptide repeat-like helical domain. NCA1 interacts with and increases catalase activity maximally in a 240-kD complex in planta. In vitro, NCA1 interacts with CATALASE2 (CAT2) in a 1:1 molar ratio, and the NCA1 C terminus is essential for this interaction. CAT2 activity increased 10-fold in the presence of NCA1, and zinc ion binding of the NCA1 N terminus is required for this increase. NCA1 has chaperone protein activity that may maintain the folding of catalase in a functional state. NCA1 is a cytosol-located protein. Expression of NCA1 in the mitochondrion of the nca1-3 mutant does not rescue the abiotic stress phenotypes of the mutant, while expression in the cytosol or peroxisome does. Our results suggest that NCA1 is essential for catalase activity.

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This paper investigates the path-tracking control issue for autonomous ground vehicles with the integral sliding mode control (ISMC) considering the transient performance improvement. The path-tracking control is converted into the yaw stabilization problem, where the sideslip-angle compensation is adopted to reduce the steady-state errors, and then the yaw-rate reference is generated for the path-tracking purpose. The lateral velocity and roll angle are estimated with the measurement of the yaw rate and roll rate. Three contributions have been made in this paper: 1) To enhance the estimation accuracy for the vehicle states in the presence of the parametric uncertainties caused by the lateral and roll dynamics, a robust extended Kalman filter is proposed based on the minimum model error algorithm; 2) An improved adaptive radial basis function neural network (RBFNN) considering the approximation error adaptation is developed to compensate for the uncertainties caused by the vertical motion;3) The RBFNN and composite nonlinear feedback (CNF)-based ISMC is developed to achieve the yaw stabilization and enhance the transient tracking performance considering the input saturation of the front steering angle. The overall stability is proved with Lyapunov function. Finally, the superiority of the developed control strategy is verified by comparing with the traditional CNF with high-fidelity CarSim-Matlab simulations.Index Terms-Path tracking, autonomous vehicles, sliding mode control, extended Kalman filter, neural network. I. INTRODUCTIONA UTONOMOUS ground vehicles (AGVs) have attracted large amounts of attention and investment in both the automotive academia and industry over the past decade, due to the gradually increasing demands for the better safety and efficiency in the mobility [1]-[3]. The emergence of AGVs is intended for improving the transportation efficiency, utilization, and most importantly, road security. Rapid advancement in the vehicular cyber-physical system (CPS) and

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