Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection

Inference of interaction rules of animals moving in groups usually relies on an analysis of large scale system behaviour. Models are tuned through repeated simulation until they match the observed behaviour. More recent work has used the fine scale motions of animals to validate and fit the rules of interaction of animals in groups. Here, we use a Bayesian methodology to compare a variety of models to the collective motion of glass prawns (Paratya australiensis). We show that these exhibit a stereotypical ‘phase transition’, whereby an increase in density leads to the onset of collective motion in one direction. We fit models to this data, which range from: a mean-field model where all prawns interact globally; to a spatial Markovian model where prawns are self-propelled particles influenced only by the current positions and directions of their neighbours; up to non-Markovian models where prawns have ‘memory’ of previous interactions, integrating their experiences over time when deciding to change behaviour. We show that the mean-field model fits the large scale behaviour of the system, but does not capture fine scale rules of interaction, which are primarily mediated by physical contact. Conversely, the Markovian self-propelled particle model captures the fine scale rules of interaction but fails to reproduce global dynamics. The most sophisticated model, the non-Markovian model, provides a good match to the data at both the fine scale and in terms of reproducing global dynamics. We conclude that prawns' movements are influenced by not just the current direction of nearby conspecifics, but also those encountered in the recent past. Given the simplicity of prawns as a study system our research suggests that self-propelled particle models of collective motion should, if they are to be realistic at multiple biological scales, include memory of previous interactions and other non-Markovian effects

Study of e+e−→ppˉe^+e^- \rightarrow p\bar{p} in the vicinity of ψ(3770)\psi(3770)

Using 2917 $\rm{pb}^{-1}$ of data accumulated at 3.773~$\rm{GeV}$, 44.5~$\rm{pb}^{-1}$ of data accumulated at 3.65~$\rm{GeV}$ and data accumulated during a $\psi(3770)$ line-shape scan with the BESIII detector, the reaction $e^+e^-\rightarrow p\bar{p}$ is studied considering a possible interference between resonant and continuum amplitudes. The cross section of $e^+e^-\rightarrow\psi(3770)\rightarrow p\bar{p}$, $\sigma(e^+e^-\rightarrow\psi(3770)\rightarrow p\bar{p})$, is found to have two solutions, determined to be ($0.059\pm0.032\pm0.012$) pb with the phase angle $\phi = (255.8\pm37.9\pm4.8)^\circ$ ($<$0.11 pb at the 90% confidence level), or $\sigma(e^+e^-\rightarrow\psi(3770)\rightarrow p\bar{p}) = (2.57\pm0.12\pm0.12$) pb with $\phi = (266.9\pm6.1\pm0.9)^\circ$ both of which agree with a destructive interference. Using the obtained cross section of $\psi(3770)\rightarrow p\bar{p}$, the cross section of $p\bar{p}\rightarrow \psi(3770)$, which is useful information for the future PANDA experiment, is estimated to be either ($9.8\pm5.7$) nb ($<17.2$ nb at 90% C.L.) or $(425.6\pm42.9)$ nb

The Nonlinear Characteristic of Vibration Signal in Milling flutter process

Abstract. In order to analysis the nonlinear characteristics of vibration signal in the milling chatter process, this article uses Lyapunov Exponent and Approximate Entropy to analysis vibration signal of stable milling, vibration signal of chatter development and vibration signal of chatter in varied cutting-depth milling process. Plot the relationship curves of milling parameters with Lyapunov Exponent and Approximate Entropy respectively. The results showed that vibration signal had strong chaos characteristics when the milling chatter developed and occurred, and chaos characteristics of the vibration signal can be regarded as an effective method to identify the development and occurrence of chatter, and it also has guiding function for choosing milling parameter. Intorduction Chatter is a kind of strong self-excited vibration which occurs in the milling process and it is the main factor of restricting cutting efficiency, reducing machining accuracy of workpiece, increasing the fatigue of cutting tools. In milling process, the detected vibration signal may arise multivalued property of response and mutagenicity of state and it has bifurcation and nonlinear characteristics of chaos[1]. If underlying chatter is found by analyzing the law of nonlinear characteristics of vibratio

Tribological properties of γ-Y2Si2O7 ceramic against AISI 52100 steel and Si3N4 ceramic counterparts

Reciprocating ball-on-flat dry sliding friction and wear experiments have been conducted on singlephase γ-Y2Si2O7 ceramic flats in contact with AISI 52100 bearing steel and Si3N4 ceramic balls at 5-15N normal loads in an ambient environment. The kinetic friction coefficients of γ-Y2Si2O7 varied in the range over 0.53-0.63 against AISI 52100 steel and between 0.51-0.56 against Si3N4 ceramic. We found thatwear occurred predominantly during the running-in period and it almost ceased at the steady friction stage. The wear rates of γ-Y2Si2O7 were in the order of 10-4mm3/(N m). Besides, wear debris strongly influenced the friction and wear processes. The strong chemical affinity between γ-Y2Si2O7 and AISI 52100 balls led to a thick transfer layer formed on both contact surfaces of the flat and counterpart ball, which changed the direct sliding between the ball and the flat into a shearing within the transfer layer. For the γ-Y2Si2O7/Si3N4 pair, a thin silica hydrate lubricant tribofilm presented above the compressed debris entrapped in the worn track and contact ball surface. This transfer layer and the tribofilm separated the sliding couple from direct contact and contributed to the low friction coefficient and wear rate