11,347 research outputs found
Exotic mesons from quantum chromodynamics with improved gluon and quark actions on the anisotropic lattice
Hybrid (exotic) mesons, which are important predictions of quantum
chromodynamics (QCD), are states of quarks and anti-quarks bound by excited
gluons. First principle lattice study of such states would help us understand
the role of ``dynamical'' color in low energy QCD and provide valuable
information for experimental search for these new particles. In this paper, we
apply both improved gluon and quark actions to the hybrid mesons, which might
be much more efficient than the previous works in reducing lattice spacing
error and finite volume effect. Quenched simulations were done at
and on a anisotropic lattice using our PC cluster. We
obtain MeV for the mass of the hybrid meson
in the light quark sector, and Mev in the
charm quark sector; the mass splitting between the hybrid meson in the charm quark sector and the spin averaged S-wave charmonium mass
is estimated to be MeV. As a byproduct, we obtain MeV for the mass of a P-wave or
meson and MeV for the mass of a P-wave meson, which are comparable to their experimental value 1426 MeV for the
meson. The first error is statistical, and the second one is
systematical. The mixing of the hybrid meson with a four quark state is also
discussed.Comment: 12 pages, 3 figures. Published versio
The curvature-induced gauge potential and the geometric momentum for a particle on a hypersphere
For a particle that is constrained to freely move on a hypersurface, the
curvature of the surface can induce a gauge potential; and for a particle on
the hypersphere, the gauge potential derived from the \textit{generalized
angular momentum algebra }on it has been known long before (J. Math. Phys.
\textbf{34}(1993)2827). We demonstrate that the momentum for the particle on
the hyperspherecan be the geometric one which obey commutation relations , in which is the
Planck's constant, and () symbolizes the th component
of the geometric momentum, and specifies the th component of the
angular momentum containing the spin-curvature coupling, and denotes the
radius of the dimensional hypersphere.Comment: 6 pages, no figur
A community merger of optimization algorithm to extract overlapping communities in networks
© 2018 IEEE. A community in networks is a subset of vertices primarily connecting internal components, yet less connecting to the external vertices. The existing algorithms aim to extract communities of the topological features in networks. However, the edges of practical complex networks involving a weight that represents the tightness degree of connection and robustness, which leads a significant influence on the accuracy of community detection. In our study, we propose an overlapping community detection method based on the seed expansion strategy applying to both the unweighted and the weighted networks, called OCSE. First, it redefines the edge weight and the vertex weight depending on the influence of the network topology and the original edge weight, and then selects the seed vertices and updates the edges weight. Comparisons between OCSE approach and existing community detection methods on synthetic and real-world networks, the results of the experiment show that our proposed approach has the significantly better performance in terms of the accuracy
Levinson's theorem for the Schr\"{o}dinger equation in two dimensions
Levinson's theorem for the Schr\"{o}dinger equation with a cylindrically
symmetric potential in two dimensions is re-established by the Sturm-Liouville
theorem. The critical case, where the Schr\"{o}dinger equation has a finite
zero-energy solution, is analyzed in detail. It is shown that, in comparison
with Levinson's theorem in non-critical case, the half bound state for
wave, in which the wave function for the zero-energy solution does not decay
fast enough at infinity to be square integrable, will cause the phase shift of
wave at zero energy to increase an additional .Comment: Latex 11 pages, no figure and accepted by P.R.A (in August); Email:
[email protected], [email protected]
Global identification of electrical and mechanical parameters in PMSM drive based on dynamic self-learning PSO
A global parameter estimation method for a PMSM drive system is proposed, where the electrical parameters, mechanical parameters and voltage-source-inverter (VSI) nonlinearity are regarded as a whole and parameter estimation is formulated as a single parameter optimization model. A dynamic learning estimator is proposed for tracking the electrical parameters, mechanical parameters and VSI of PMSM drive by using dynamic self learning particle swarm optimization (DSLPSO). In DSLPSO, a novel movement modification equation with dynamic exemplar learning strategy is designed to ensure its diversity and achieve a reasonable tradeoff between the exploitation and exploration during the search process. Moreover, a nonlinear multi-scale based interactive learning operator is introduced for accelerating the convergence speed of the Pbest particles; meanwhile a dynamic opposition-based learning (OBL) strategy is designed to facilitate the gBest particle to explore a potentially better region. The proposed algorithm is applied to parameter estimation for a PMSM drive system. The results show that the proposed method has better performance in tracking the variation of electrical parameters, and estimating the immeasurable mechanical parameters and the VSI disturbance voltage simultaneously
The secretion of proinflammatory cytokines and chemokines in stimulated fibroblast-like synoviocytes of osteoarthritis
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