Imperfect pitchfork bifurcation in asymmetric two-compartment granular gas

The clustering behavior of a mono-disperse granular gas is experimentally studied in an asymmetric two-compartment setup. Unlike the random clustering in either compartment in the case of symmetric configuration when lowering the shaking strength to below a critical value, the directed clustering is observed, which corresponds to an imperfect pitchfork bifurcation. Numerical solutions of the flux equation using a modified simple flux function show qualitative agreements with the experimental results. The potential application of this asymmetric structure is discussed.

Fluidization science,its development and future

By revisiting the three stage theory for the progress of science proposed by Taketani in 1942, the footmarks of fluidization research are examined. The bubbling and fast fluidization issues were emphasized so that the future offluidization research can be discussed among scientists and engineers in a wider perspective. The first cycle of fluidization research was started in the early 1940s by an initial stage of phenomenology. The second stage of structural studies was kicked off in the early 1950s with the introduction of the two phase theory. The third stage of essential studies occurred in the early 1960s in the form of bubble hydrodynamics. The second cycle, which confirmed the aforementioned three stages closed at the turn of the century, established a general understanding of suspension structures including agglomerating fluidization, bubbling, turbulent and fast fluidizations and pneumatic transport; also established powerful measurement and numerical simulation tools.After a general remark on science, technology and society issues the interactions between fluidization technology and science are revisited. Our future directions are discussed including the tasks in the third cycle, particularly in its phenomenology stage where strong motivation and intention are always necessary, in relation also to the green reforming of the present technology. A generalized definition of ＇fluidization＇ is proposed to extend fluidization principle into much wider scientific fields, which would be effective also for wider collaborations.

A novel prediction model of traffic accidents based on big data

The occurrence of traffic accidents is regular in probability distribution.Using big data mining method to predict traffic accidents is conducive to taking measures to prevent or reduce traffic accidents in advance.In recent years,prediction methods of traffic accidents used by researchers have some problems,such as low calculation accuracy.Therefore,a prediction model of traffic accidents based on joint probability density feature extraction of big data is proposed in this paper.First,a function of big data joint probability distribution for traffic accidents is established.Second,establishing big data distributed database model of traffic accidents with the statistical analysis method in order to mine the association rules characteristic quantity reflecting the law of traffic accidents,and then extracting the joint probability density feature of big data for traffic accident probability distribution.According to the result of feature extraction,adaptive functional and directivity are predicted,and then the regularity prediction of traffic accidents is realized based on the result of association directional clustering,so as to optimize the design of the prediction model of traffic accidents based on big data.Simulation results show that in predicting traffic accidents,the model in this paper has advantages of relatively high accuracy,relatively good confidence and stable prediction result.