Deconfinement Phase Transition Heating and Thermal Evolution of Neutron Stars

The deconfinement phase transition will lead to the release of latent heat during spins down of neutron stars if the transition is the first-order one.We have investigated the thermal evolution of neutron stars undergoing such deconfinement phase transition. The results show that neutron stars may be heated to higher temperature.This feature could be particularly interesting for high temperature of low-magnetic field millisecond pulsar at late stage.Comment: 4 pages, to be published by American Institute of Physics, ed. D.Lai, X.D.Li and Y.F.Yuan, as the Proceedings of the conference Astrophysics of Compact Object

Neuroprotective Effect of Phosphocreatine on Focal Cerebral Ischemia-Reperfusion Injury

Phosphocreatine (PCr) is a natural compound, which can donate high-energy phosphate group to ADP to synthesize ATP, even in the absence of oxygen and glucose. At present, it is widely used in cardiac and renal ischemia-reperfusion (IR) disease. In this study, to examine the protective efficacy of PCr against cerebral IR, disodium creatine phosphate was injected intravenously into rats before focal cerebral IR. Intracranial pressure (ICP), neurological score, cerebral infarction volume, and apoptotic neurons were observed. Expression of caspase-3 and aquaporin-4 (AQP4) was analyzed. Compared with IR group, rats pretreated with PCr had better neurologic score, less infarction volume, fewer ultrastructural histopathologic changes, reduced apoptosis, and lower aquaporin-4 level. In conclusion, PCr is neuroprotective after transient focal cerebral IR injury. Such a protection might be associated with apoptosis regulating proteins

A Short Overview of 6G V2X Communication Standards

We are on the verge of a new age of linked autonomous cars with unheard-of user experiences, dramatically improved air quality and road safety, extremely varied transportation settings, and a plethora of cutting-edge apps. A substantially improved Vehicle-to-Everything (V2X) communication network that can simultaneously support massive hyper-fast, ultra-reliable, and low-latency information exchange is necessary to achieve this ambitious goal. These needs of the upcoming V2X are expected to be satisfied by the Sixth Generation (6G) communication system. In this article, we start by introducing the history of V2X communications by giving details on the current, developing, and future developments. We compare the applications of communication technologies such as Wi-Fi, LTE, 5G, and 6G. we focus on the new technologies for 6G V2X which are brain-vehicle interface, blocked-based V2X, and Machine Learning (ML). To achieve this, we provide a summary of the most recent ML developments in 6G vehicle networks. we discuss the security challenges of 6G V2X. We address the strengths, open challenges, development, and improving areas of further study in this field.Comment: 7 pages, 2 figures, IEEE ICN 202

Quantum algorithms for matrix geometric means

Matrix geometric means between two positive definite matrices can be defined equivalently from distinct perspectives - as solutions to certain nonlinear systems of equations, as points along geodesics in Riemannian geometry, and as solutions to certain optimisation problems. This diversity already suggests the potential for varied applications, as well as acting as a bridge between different domains. Here we devise new quantum subroutines to efficiently prepare quantum unitary operators that embed the standard matrix geometric mean and its generalisations called the weighted matrix geometric mean. This enables the construction of solutions to the algebraic Riccati equation, which is an important class of nonlinear systems of equations that appears in machine learning, optimal control, estimation, and filtering. Using these subroutines, we present a new class of quantum learning algorithms called quantum geometric mean metric learning. This has applications in efficiently finding the best distance measure and solving classification problems in the weakly supervised limit and for anomaly detection, for both classical and quantum problems. We also show how our method can be generalised to a particular p^th-order system of nonlinear equations. These quantum subroutines for matrix geometric means are also useful in other areas of quantum information. For example, we show how to use them in the estimation of geometric Renyi relative entropies and the Uhlmann fidelity by means of the Fuchs-Caves observable. In particular, our quantum algorithms for estimating the Uhlmann and Matsumoto fidelities have optimal dependence on the precision. Finally, we provide a BQP-complete problem based on matrix geometric means that can be solved by our subroutines, thus characterising their computational capability