A novel model was developed to theoretically evaluate the 02 adsorption on H-terminated Si（001）-（2×2×1） surface. The periodic boundary condition, the ultrasoft pseudopotentials technique based on density...A novel model was developed to theoretically evaluate the 02 adsorption on H-terminated Si（001）-（2×2×1） surface. The periodic boundary condition, the ultrasoft pseudopotentials technique based on density functional theory （DFT） with generalized gradient approxi,natior, （GGA） functional were applied in our ab initio calculations. By analyzing bonding energy oil site, the favourable adsorption site was determined. The calculations also predicted that the adsorption products should be Si=O and H2O. This theoretical study snpported the reaction mechanism provided by Kovalev et al, The results were also a base for further investigation of some more complex systems such as the oxida.tion on porous silicon surface.展开更多
The flash points of organic compounds were estimated using a hybrid method that includes a simple group contribution method (GCM) implemented in an artificial neural network (ANN) with particle swarm optimization (PSO...The flash points of organic compounds were estimated using a hybrid method that includes a simple group contribution method (GCM) implemented in an artificial neural network (ANN) with particle swarm optimization (PSO). Different topologies of a multilayer neural network were studied and the optimum architecture was determined. Property data of 350 compounds were used for training the network. To discriminate different substances the molecular structures defined by the concept of the classical group contribution method were given as input variables. The capabilities of the network were tested with 155 substances not considered in the training step. The study shows that the proposed GCM+ANN+PSO method represent an excellent alternative for the estimation of flash points of organic compounds with acceptable accuracy (AARD = 1.8%; AAE = 6.2 K).展开更多
Different driving decisions will cause different processes of phase transition in traffic flow. To reveal the inner mechanism, this paper built a new cellular automaton （CA） model, based on the driving decision （DD...Different driving decisions will cause different processes of phase transition in traffic flow. To reveal the inner mechanism, this paper built a new cellular automaton （CA） model, based on the driving decision （DD）. In the DD model, a driver＇s decision is divided into three stages： decision-making, action, and result. The acceleration is taken as a decision variable and three core factors, i.e. distance between adjacent vehicles, their own velocity, and the preceding vehicle＇s velocity, are considered. Simulation results show that the DD model can simulate the synchronized flow effectively and describe the phase transition in traffic flow well. Further analyses illustrate that various density will cause the phase transition and the random probability will impact the process. Compared with the traditional NaSch model, the DD model considered the preceding vehicle＇s velocity, the deceleration limitation, and a safe distance, so it can depict closer to the driver preferences on pursuing safety, stability and fuel-saving and has strong theoretical innovation for future studies.展开更多
文摘A novel model was developed to theoretically evaluate the 02 adsorption on H-terminated Si（001）-（2×2×1） surface. The periodic boundary condition, the ultrasoft pseudopotentials technique based on density functional theory （DFT） with generalized gradient approxi,natior, （GGA） functional were applied in our ab initio calculations. By analyzing bonding energy oil site, the favourable adsorption site was determined. The calculations also predicted that the adsorption products should be Si=O and H2O. This theoretical study snpported the reaction mechanism provided by Kovalev et al, The results were also a base for further investigation of some more complex systems such as the oxida.tion on porous silicon surface.
文摘The flash points of organic compounds were estimated using a hybrid method that includes a simple group contribution method (GCM) implemented in an artificial neural network (ANN) with particle swarm optimization (PSO). Different topologies of a multilayer neural network were studied and the optimum architecture was determined. Property data of 350 compounds were used for training the network. To discriminate different substances the molecular structures defined by the concept of the classical group contribution method were given as input variables. The capabilities of the network were tested with 155 substances not considered in the training step. The study shows that the proposed GCM+ANN+PSO method represent an excellent alternative for the estimation of flash points of organic compounds with acceptable accuracy (AARD = 1.8%; AAE = 6.2 K).
基金Supported by the Program for National High-Tech Research and Development Program of China under Grant No 2007AA11Z233National Key Technology R & D Program under Grant No. 2009BAG13A06China Postdoctoral Science Foundation Funded Project under Grant No. 20090450395
文摘Different driving decisions will cause different processes of phase transition in traffic flow. To reveal the inner mechanism, this paper built a new cellular automaton （CA） model, based on the driving decision （DD）. In the DD model, a driver＇s decision is divided into three stages： decision-making, action, and result. The acceleration is taken as a decision variable and three core factors, i.e. distance between adjacent vehicles, their own velocity, and the preceding vehicle＇s velocity, are considered. Simulation results show that the DD model can simulate the synchronized flow effectively and describe the phase transition in traffic flow well. Further analyses illustrate that various density will cause the phase transition and the random probability will impact the process. Compared with the traditional NaSch model, the DD model considered the preceding vehicle＇s velocity, the deceleration limitation, and a safe distance, so it can depict closer to the driver preferences on pursuing safety, stability and fuel-saving and has strong theoretical innovation for future studies.
