A magnetically driven origin for the low luminosity GRB 170817A associated with GW170817

The gamma-ray burst GRB 170817A associated with GW170817 is subluminous and subenergetic compared with other typical short GRBs. It may be due to a relativistic jet viewed off-axis, or a structured jet, or cocoon emission. Giant flares from magnetars may possibly be ruled out. However, the luminosity and energetics of GRB 170817A is coincident with that of magnetar giant flares. After the coalescence of the binary neutron star, a hypermassive neutron star may be formed. The hypermassive neutron star may have magnetar-strength magnetic field. During the collapse of the hypermassive neutron star, the magnetic field energy will also be released. This giant-flare-like event may explain the the luminosity and energetics of GRB 170817A. Bursts with similar luminosity and energetics are expected in future neutron star-neutron star or neutron star-black hole mergers.Comment: 6 pages, 1 figure, accepted in Research in Astronomy and Astrophysic

Cosmic age, Statefinder and OmOm diagnostics in the decaying vacuum cosmology

As an extension of $\Lambda$CDM, the decaying vacuum model (DV) describes the dark energy as a varying vacuum whose energy density decays linearly with the Hubble parameter in the late-times, $\rho_\Lambda(t) \propto H(t)$, and produces the matter component. We examine the high-$z$ cosmic age problem in the DV model, and compare it with $\Lambda$CDM and the Yang-Mills condensate (YMC) dark energy model. Without employing a dynamical scalar field for dark energy, these three models share a similar behavior of late-time evolution. It is found that the DV model, like YMC, can accommodate the high-$z$ quasar APM 08279+5255, thus greatly alleviates the high-$z$ cosmic age problem. We also calculate the Statefinder $(r,s)$ and the {\it Om} diagnostics in the model. It is found that the evolutionary trajectories of $r(z)$ and $s(z)$ in the DV model are similar to those in the kinessence model, but are distinguished from those in $\Lambda$CDM and YMC. The ${\it Om}(z)$ in DV has a negative slope and its height depends on the matter fraction, while YMC has a rather flat ${\it Om}(z)$, whose magnitude depends sensitively on the coupling.Comment: 12 pages, 4 figures, with some correction

Separable states and the geometric phases of an interacting two-spin system

It is known that an interacting bipartite system evolves as an entangled state in general, even if it is initially in a separable state. Due to the entanglement of the state, the geometric phase of the system is not equal to the sum of the geometric phases of its two subsystems. However, there may exist a set of states in which the nonlocal interaction does not affect the separability of the states, and the geometric phase of the bipartite system is then always equal to the sum of the geometric phases of its subsystems. In this paper, we illustrate this point by investigating a well known physical model. We give a necessary and sufficient condition in which a separable state remains separable so that the geometric phase of the system is always equal to the sum of the geometric phases of its subsystems.Comment: 13 page

Geometric phase in open systems: beyond the Markov approximation and weak coupling limit

Beyond the quantum Markov approximation and the weak coupling limit, we present a general theory to calculate the geometric phase for open systems with and without conserved energy. As an example, the geometric phase for a two-level system coupling both dephasingly and dissipatively to its environment is calculated. Comparison with the results from quantum trajectory analysis is presented and discussed

Innovative Hybridisation of Genetic Algorithms and Neural Networks in Detecting Marker Genes for Leukaemia Cancer

Methods for extracting marker genes that trigger the growth of cancerous cells from a high level of complexity microarrays are of much interest from the computing community. Through the identified genes, the pathology of cancerous cells can be revealed and early precaution can be taken to prevent further proliferation of cancerous cells. In this paper, we propose an innovative hybridised gene identification framework based on genetic algorithms and neural networks to identify marker genes for leukaemia disease. Our approach confirms that high classification accuracy does not ensure the optimal set of genes have been identified and our model delivers a more promising set of genes even with a lower classification accurac