Based on quantum renormalization group (QRG) method, we investigated quantum coherence and quantum phase transition (QPT) in XXZ chain and XY chain, respectively. The results show that both the geometric quantum coherence and entropic coherecne can accurately indicate the QPT at critical point after enough iteration steps. Moreover, the increasing anisotropy parameter destroys the coherence in the XXZ chain, while enhances it in the XY chain. In addition, focused on the XXZ chain we analyzed the nonanalytic phenomena and scaling behaviors with different theoretical exponents in detail.
In this work we extend the method of the constrained large-eddy simulation(CLES) to simulate the turbulent flow over inhomogeneous rough walls. In the original concept of CLES, the subgrid-scale(SGS) stress is constrained so that the mean part and the fluctuation part of the SGS stress can be modelled separately to improve the accuracy of the simulation result. Here in the simulation of the rough-wall flows, we propose to interpret the extra stress terms in the CLES formulation as the roughness-induced stress so that the roughness inhomogeneity can be incorporated by modifying the formulation of the constrained SGS stress. This is examined with the simulations of the channel flow with the spanwise alternating high/low roughness strips. Then the CLES method is employed to investigate the temporal response of the turbulence to the change of the wall condition from rough to smooth. We demonstrate that the temporal development of the internal boundary layer is just similar to that in a spatial rough-tosmooth transition process, and the spanwise roughness inhomogeneity has little impact on the transition process. 相似文献
The Coronavirus disease 2019 (COVID-19) has become one of the threats to the world. Computed tomography (CT) is an informative tool for the diagnosis of COVID-19 patients. Many deep learning approaches on CT images have been proposed and brought promising performance. However, due to the high complexity and non-transparency of deep models, the explanation of the diagnosis process is challenging, making it hard to evaluate whether such approaches are reliable. In this paper, we propose a visual interpretation architecture for the explanation of the deep learning models and apply the architecture in COVID-19 diagnosis. Our architecture designs a comprehensive interpretation about the deep model from different perspectives, including the training trends, diagnostic performance, learned features, feature extractors, the hidden layers, the support regions for diagnostic decision, and etc. With the interpretation architecture, researchers can make a comparison and explanation about the classification performance, gain insight into what the deep model learned from images, and obtain the supports for diagnostic decisions. Our deep model achieves the diagnostic result of 94.75%, 93.22%, 96.69%, 97.27%, and 91.88% in the criteria of accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, which are 8.30%, 4.32%, 13.33%, 10.25%, and 6.19% higher than that of the compared traditional methods. The visualized features in 2-D and 3-D spaces provide the reasons for the superiority of our deep model. Our interpretation architecture would allow researchers to understand more about how and why deep models work, and can be used as interpretation solutions for any deep learning models based on convolutional neural network. It can also help deep learning methods to take a step forward in the clinical COVID-19 diagnosis field. 相似文献
To control the temporal profile of a relativistic electron beam to meet requirements of various advanced scientific applications like free-electron-laser and plasma wakefield acceleration, a widely-used technique is to manipulate the dispersion terms which turns out to be one-to-many problems. Due to their intrinsic one-to-many property, current popular stochastic optimization approaches on temporal shaping may face the problems of long computing time or sometimes suggesting only one solution. Here we propose a real-time solver for one-to-many problems of temporal shaping, with the aid of a semi-supervised machine learning method, the conditional generative adversarial network (CGAN). We demonstrate that the CGAN solver can learn the one-to-many dynamics and is able to accurately and quickly predict the required dispersion terms for different custom temporal profiles. This machine learning-based solver is expected to have the potential for wide applications to one-to-many problems in other scientific fields. 相似文献
By linking the carbazole unit to the nitrogen atom of acridone through phenyl or pyridyl, two compounds, named 10-(4-(9H-carbazol-9-yl)phenyl)acridin-9(10H)-one (AC-Ph-Cz) and 10-(5-(9H-carbazol-9-yl)pyridin-2-yl)acridin-9(10H)-one (AC-Py-Cz) were designed and synthesized. These two materials, characterized with highly twisted and rigid structure, good thermal stability, and balanced carrier-transporting properties, were employed as host materials for green phosphorescent and thermally activated delayed fluorescent organic light-emitting diodes (OLEDs). The carbazole group, despite its small contribution to the highest occupied molecular orbitals (HOMOs) of these two materials, plays an essential role as an intramolecular host in energy delivering and improving the hole transporting ability of these two hosts. The incorporation of the electron-deficient pyridyl group as a linking group slightly improves the electron transporting capability of AC-Py-Cz. The green phosphorescent OLED (PhOLED) based on AC-Py-Cz exhibited excellent device performance with a turn-on voltage of 2.5 V, a maximum power efficiency and an external quantum efficiency (ηext) of 89.8 lm W−1 and 25.2 %, respectively, benefitting from the better charge-balancing ability of AC-Py-Cz host due to the presence of the pyridyl bridge. More importantly, all the devices based on these two hosts showed low efficiency roll-off at high brightness due to the suppressed non-radiative transition in the emitting layer. In particular, the AC-Py-Cz-hosted green PhOLED exhibited an efficiency roll-off of 1.6 % from the maximum next at a high brightness of 1000 cd m−2 and a roll-off of 15.9 % at an extremely high brightness of 10000 cd m−2. This study manifests that acridone-based host materials have great potential in fabricating OLEDs with low efficiency roll-off. 相似文献
We study the mechanism of van der Waals(vdW)interactions on phonon transport in atomic scale,which would boost developments in heat management and energy conversion.Commonly,the vdW interactions are regarded as a hindrance in phonon transport.Here we propose that the vdW confinement can enhance phonon transport.Through molecular dynamics simulations,it is realized that the vdW confinement is able to make more than two-fold enhancement on thermal conductivity of both polyethylene single chain and graphene nanoribbon.The quantitative analyses of morphology,local vdW potential energy and dynamical properties are carried out to reveal the underlying physical mechanism.It is found that the confined vdW potential barriers reduce the atomic thermal displacement magnitudes,leading to less phonon scattering and facilitating thermal transport.Our study offers a new strategy to modulate the phonon transport. 相似文献
Science China Mathematics - We prove that if a compact Riemannian 4-manifold with positive sectional curvature satisfies a strengthened Kato type inequality, then it is definite. We also discuss... 相似文献
Stock markets in the world are linked by complicated and dynamical relationships into a temporal network.Extensive works have provided us with rich findings from the topological properties and their evolutionary trajectories,but the underlying dynamical mechanism is still not in order.In the present work,we proposed a technical scheme to reveal the dynamical law from the temporal network.The index records for the global stock markets form a multivariate time series.One separates the series into segments and calculates the information flows between the markets,resulting in a temporal market network representing the state and its evolution.Then the technique of the Koopman decomposition operator is adopted to find the law stored in the information flows.The results show that the stock market system has a high flexibility,i.e.,it jumps easily between different states.The information flows mainly from high to low volatility stock markets.And the dynamical process of information flow is composed of many dynamic modes distribute homogenously in a wide range of periods from one month to several ten years,but there exist only nine modes dominating the macroscopic patterns. 相似文献
We show, contrary to some published statements, that spectral synthesis does not generally hold for commutative semigroups that are not groups. On the positive side we prove that it holds if the semigroup is a monoid with no prime ideal. For semigroups with a prime ideal, the picture is not so clear. On the negative side we provide a variety of examples illustrating the failure of spectral synthesis for many semigroups with prime ideals, but we also give examples of semigroups with prime ideals on which spectral synthesis holds.
Nonlinear Dynamics - This paper investigates a Hopfield neural network under the simulation of external electromagnetic radiation and dual bias currents, in which the fluctuation of magnetic flux... 相似文献