On the momentum-dependence of K−K^{-}-nuclear potentials

The momentum dependent $K^{-}$-nucleus optical potentials are obtained based on the relativistic mean-field theory. By considering the quarks coordinates of $K^-$ meson, we introduced a momentum-dependent "form factor" to modify the coupling vertexes. The parameters in the form factors are determined by fitting the experimental $K^{-}$-nucleus scattering data. It is found that the real part of the optical potentials decrease with increasing $K^-$ momenta, however the imaginary potentials increase at first with increasing momenta up to $P_k=450\sim 550$ MeV and then decrease. By comparing the calculated $K^-$ mean free paths with those from $K^-n$/$K^-p$ scattering data, we suggested that the real potential depth is $V_0\sim 80$ MeV, and the imaginary potential parameter is $W_0\sim 65$ MeV.Comment: 9 pages, 4 figure

STAND: A Spatio-Temporal Algorithm for Network Diffusion Simulation

Information, ideas, and diseases, or more generally, contagions, spread over space and time through individual transmissions via social networks, as well as through external sources. A detailed picture of any diffusion process can be achieved only when both a good network structure and individual diffusion pathways are obtained. The advent of rich social, media and locational data allows us to study and model this diffusion process in more detail than previously possible. Nevertheless, how information, ideas or diseases are propagated through the network as an overall process is difficult to trace. This propagation is continuous over space and time, where individual transmissions occur at different rates via complex, latent connections. To tackle this challenge, a probabilistic spatiotemporal algorithm for network diffusion (STAND) is developed based on the survival model in this research. Both time and spatial distance are used as explanatory variables to simulate the diffusion process over two different network structures. The aim is to provide a more detailed measure of how different contagions are transmitted through various networks where nodes are geographic places at a large scale

Optimization of scale-free network for random failures

It has been found that the networks with scale-free distribution are very resilient to random failures. The purpose of this work is to determine the network design guideline which maximize the network robustness to random failures with the average number of links per node of the network is constant. The optimal value of the distribution exponent and the minimum connectivity to different network size are given in this paper. Finally, the optimization strategy how to improve the evolving network robustness is given.Comment: 6 pages, 1 figur

Optimization of robustness of scale-free network to random and targeted attacks

The scale-fee networks, having connectivity distribution $P(k)\sim k^{-\alpha}$ (where $k$ is the site connectivity), is very resilient to random failures but fragile to intentional attack. The purpose of this paper is to find the network design guideline which can make the robustness of the network to both random failures and intentional attack maximum while keeping the average connectivity per node constant. We find that when $=3$ the robustness of the scale-free networks reach its maximum value if the minimal connectivity $m=1$, but when is larger than four, the networks will become more robust to random failures and targeted attacks as the minimal connectivity $m$ gets larger

2-Meth­oxy-N′-(2-methoxy­benzyl­idene)benzohydrazide

The title Schiff base compound, C16H16N2O3, was derived from the condensation of 2-methoxy­benzaldehyde with 2-methoxy­benzohydrazide in an ethanol solution. The dihedral angle between the two aromatic rings is 87.5 (3)°. In the crystal structure, the mol­ecules are linked into chains running parallel to the a axis by inter­molecular N—H⋯O hydrogen bonds

Multiplex Limited Penetrable Horizontal Visibility Graph from EEG Signals for Driver Fatigue Detection

This work was supported by National Natural Science Foundation of China under Grant Nos. 61473203, 61873181 and the Natural Science Foundation of Tianjin, China under Grant No. 16JCYBJC18200.Peer reviewedPostprin