A Total Generalized Optimal Velocity Model and Its Numerical Tests

A car-following model named total generalized optimal velocity model （TGOVM） was developed with a consideration of an arbitrary number of preceding vehicles before current one based on analyzing the previous models such as optimal velocity model （OVM）, generalized OVM （GOVM） and improved GOVM （IGOVM）. This model describes the physical phenomena of traffic flow more exactly and realistically than previous models. Also the performance of this model was checked out by simulating the acceleration and deceleration process for a small delay time. On a single circular lane, the evolution of the traffic congestion was studied for a different number of headways and relative velocities of the preceding vehicles being taken into account. The simulation results show that TGOVM is reasonable and correct.

Motion constraint Markov network model for multi-target tracking

A typical Markov network for modeling the interaction among targets can handle the error merge problem,but it suffers from the labeling problem due to the blind competition among collaborative trackers. In this paper,we propose a motion constraint Markov network model for multi-target tracking. By augmenting the typical Markov network with an ad hoc Markov chain which carries motion constraint prior,this proposed model can overcome the blind competition and direct the label to the corresponding target even in the case of severe occlusion. In addition,the motion constraint prior is formu-lated as a local potential function and can be easily incorporated in the joint distribution representation of the novel model. Experimental results demonstrate that our model is superior to other methods in solving the error merge and labeling problems simultaneously and efficiently.

Efficient Scheme for Macroscopic Superpositions and Entanglement of High-Order Squeezed Vacuum States in Cavity QED

We propose an etticient scheme for generating the macroscopic superpositions and the entanglement between the high-order squeezed vacuum states by considering the multi-photon interaction of N two-level atoms in a cavity with high quality factor, assisted by a strong driving field. Through specific choices of the cavity detuning, a number of multi-party entangled states between the atoms and the high-order squeezed vacuum states and among the high-order squeezed vacuum states of the cavities can be prepared, including also the macroscopic ＂Schrodinger cats＂ of the high- order squeezed vacuum states, the entangled states of the macroscopic ＂Schrodinger cats＂, and so on. Possible extension and application of our scheme are discussed. Our scheme is reachable within the current techniques in the cavity QED.

Quadratic investigation of geochemical distribution by backward elimination approach at Glojeh epithermal Au(Ag)-polymetallic mineralization, NW Iran

The correspondence analysis will describe elemental association accompanying an indicator samples.This analysis indicates strong mineralization of Ag,As,Pb,Te,Mo,Au,Zn and to a lesser extent S,W,Cu at Glojeh polymetallic mineralization,NW Iran.This work proposes a backward elimination approach(BEA)that quantitatively predicts the Au concentration from main effects(X),quadratic terms(X2)and the first order interaction(Xi×Xj)of Ag,Cu,Pb,and Zn by initialization,order reduction and validation of model.BEA is done based on the quadratic model(QM),and it was eliminated to reduced quadratic model(RQM)by removing insignificant predictors.During the QM optimization process,overall convergence trend of R2,R2(adj)and R2(pred)is obvious,corresponding to increase in the R2(pred)and decrease of R2.The RQM consisted of(threshold value,Cu,Ag×Cu,Pb×Zn,and Ag2-Pb2)and(Pb,Ag×Cu,Ag×Pb,Cu×Zn,Pb×Zn,and Ag2)as main predictors of optimized model according to288and679litho-samples in trenches and boreholes,respectively.Due to the strong genetic effects with Au mineralization,Pb,Ag2,and Ag×Pb are important predictors in boreholes RQM,while the threshold value is known as an important predictor in the trenches model.The RQMs R2(pred)equal74.90%and60.62%which are verified by R2equal to73.9%and60.9%in the trenches and boreholes validation group,respectively.