Short distance potential and the thick center vortex model

The short distance potentials between heavy SU(3) and SU(4) sources are calculated by increasing the role of vortex fluxes piercing Wilson loops with contributions close to the trivial center element and by fluctuating the vortex core size in the model of thick center vortices. By this method, a Coulombic potential consistent with Casimir scaling is obtained. In addition, all other features of the potential including a linear intermediate potential in agreement with Casimir scaling and a large distance potential proportional to the $N$-ality of the representation are restored. Therefore, the model of thick center vortices may be used as a phenomenological model, which is able to describe the potential for all regimes.Comment: 9 pages and 6 figure

Confinement and the second vortex of the SU(4) gauge group

We study the potential between static SU(4) sources using the Model of Thick Center Vortices. Such vortices are characterized by the center elements $z_1=\mathrm i$ and $z_2=z_1^2$. Fitting the ratios of string tensions to those obtained in Monte-Carlo calculations of lattice QCD we get $f_2>f_1^2$, where $f_n$ is the probability that a vortex of type $n$ is piercing a plaquette. Because of $z_2=z_1^2$ vortices of type two are overlapping vortices of type one. Therefore, $f_2>f_1^2$ corresponds to the existence of an attractive force between vortices of type one

Quantitative Performance Analysis of Hybrid Mesh Segmentation

This paper presents a comprehensive quantitative performance analysis of hybrid mesh segmentation algorithm. An important contribution of this proposed hybrid mesh segmentation algorithm is that it clusters facets using “facet area” as a novel mesh attribute. The method does not require to set any critical parameters for segmentation. The performance of the proposed algorithm is evaluated by comparing the proposed algorithm with the recently developed state-of-the-art algorithms in terms of coverage, time complexity, and accuracy. The experimentation results on various benchmark test cases demonstrate that Hybrid Mesh Segmentation approach does not depend on complex attributes, and outperforms the existing state-of-the-art algorithms. The simulation reveals that Hybrid Mesh Segmentation achieves a promising performance with coverage of more than 95%