abstract

The effect of nucleon coupling constants on the moment of inertia of proto-neutron star (PNS) PSR J0740+6620 is examined with relativistic mean-field theory in consideration of a baryon octet. Here, nucleon coupling parameters DD-MEI, NL1, NL2, TW99, and GM1 are used. Under the constraints of the observed mass \(M=2.05\)–\(2.24~M_{\odot }\) of PNS PSR J0740+6620 (taking the observed mass of neutron star (NS) PSR J0740+6620), the radius of the PNS calculated by us with GM1 is the smallest, \(R=14.63\)–13.44 km, while the radius calculated with TW99 is the largest, \(R=17.46\)–17.07 km. The radii calculated from the other three sets of nucleon coupling parameters are between the two values mentioned above. The maximum value of the moment of inertia calculated with the nucleon coupling parameters TW99, NL1, DD-MEI, NL2, and GM1 decreases in turn. Under the constraint of the observed mass of PNS PSR J0740+6620 (taking the observed mass of NS PSR J0740+6620), the moment of inertia calculated with NL1, NL2, DD-MEI, and TW99 increases with the increase of the central energy density and mass, and decreases with the increase of the radius. The moment of inertia calculated with GM1 decreases with the increase of central energy density and mass, and increases with the increase of the radius. The moment of inertia of the PNS PSR J0740+6620 calculated by us with five groups of nucleon coupling parameters is between \(2.465\times 10^{45}\)–\(2.050\times 10^{45}\) g cm\(^{2}\) (GM1) and \(3.597\times 10^{45}\)–\(3.883\times 10^{45}\) g cm\(^{2}\) (TW99).

abstract

Experiments motivated by predictions of quantum mechanics indicate non-trivial correlations between spacelike-separated measurements. The phenomenon is referred to as a violation of strong-locality and, after Einstein, called ghostly action at a distance. An intriguing and previously unasked question is how the evolution of an assembly of particles to equilibrium-state relates to strong-locality. More specifically, whether, with this respect, indistinguishable particles differ from distinguishable ones. To address the question, we introduce a Markov-chain-based framework over a finite set of microstates. For the first time, we formulate conditions needed to obey the particle transport- and strong-locality for indistinguishable particles. Models which obey transport-locality and lead to equilibrium-state are considered. We show that it is possible to construct models obeying and violating strong-locality both for indistinguishable particles and for distinguishable ones. However, we find that only for distinguishable particles strongly-local evolution to equilibrium is possible without breaking the microstate-symmetry. This is the strongest symmetry one can impose and leads to the shortest equilibration time. We hope that the results presented here may provide a new perspective on a violation of strong-locality, and the developed framework will help in future studies. Specifically, they may help to interpret results on high-energy nuclear collisions indicating a fast equilibration of indistinguishable particles.

abstract

The formalism for an efficient scheme for spin-rotational symmetric matrix-product states (MPSs), also known as tensor trains (TTs), is presented. The methodology is applied for the study of ground-state energy and correlation properties of the isotropic spin-1 bilinear-biquadratic quantum Heisenberg chain with nearest-neighbor interactions and periodic boundary conditions as an example. The mathematical framework can be used for arbitrary spin-rotational invariant spin-\(S\) chains.

abstract

In the field of complex networks, Identifying crucial spreaders with high propagation ability is an important aspect of research, especially in the background of the global spread of COVID‑19. In view of this, a large number of ranking algorithms and their improved versions have been proposed to evaluate the importance of nodes in the network, such as degree centrality, betweenness centrality, and k-core centrality. However, most of these methods neglect to consider the average shortest path between important nodes in the process of node importance evaluation, which will be difficult to ensure that the initial crucial spreaders have a large influence on the network. Recently, the VoteRank algorithm proposed a new idea for identifying widely distributed key spreaders based on the voting mechanism, but there are some aspects of this algorithm that require improvement. In this paper, we propose a VoteRank improved by degree centrality, k-core, and h-index (DKHVoteRank) for identifying critical spreaders in the complex networks. We introduce additional metrics to optimize the voting mechanism of the VoteRank to ensure that our algorithm can identify a widely distributed spreaders with high importance in the network. We conducted simulation experiments based on the Susceptible–Infected–Recovered (SIR) model on 12 different complex network datasets, and the results show that our proposed algorithm performs significantly better than other benchmark algorithms in terms of propagation capability, propagation scale, and applicability of the algorithm.