Globally Polarized Quark-gluon Plasma in Non-central A+A Collisions

Produced partons have large local relative orbital angular momentum along the direction opposite to the reaction plane in the early stage of non-central heavy-ion collisions. Parton scattering is shown to polarize quarks along the same direction due to spin-orbital coupling. Such global quark polarization will lead to many observable consequences, such as left-right asymmetry of hadron spectra, global transverse polarization of thermal photons, dileptons and hadrons. Hadrons from the decay of polarized resonances will have azimuthal asymmetry similar to the elliptic flow. Global hyperon polarization is predicted within different hadronization scenarios and can be easily tested.Comment: 4 pages in RevTex with 2 postscript figures, an erratum is added to the final published versio

Solidification of Al-Sn-Cu based immiscible alloys under intense shearing

The official published version of the Article can be accessed from the link below - Copyright @ 2009 The Minerals, Metals & Materials Society and ASM InternationalThe growing importance of Al-Sn based alloys as materials for engineering applications necessitates the development of uniform microstructures with improved performance. Guided by the recently thermodynamically assessed Al-Sn-Cu system, two model immiscible alloys, Al-45Sn-10Cu and Al-20Sn-10Cu, were selected to investigate the effects of intensive melt shearing provided by the novel melt conditioning by advanced shear technology (MCAST) unit on the uniform dispersion of the soft Sn phase in a hard Al matrix. Our experimental results have confirmed that intensive melt shearing is an effective way to achieve fine and uniform dispersion of the soft phase without macro-demixing, and that such dispersed microstructure can be further refined in alloys with precipitation of the primary Al phase prior to the demixing reaction. In addition, it was found that melt shearing at 200 rpm and 60 seconds will be adequate to produce fine and uniform dispersion of the Sn phase, and that higher shearing speed and prolonged shearing time can only achieve minor further refinement.This work is funded by the EPSRC and DT

In situ photogalvanic acceleration of optofluidic kinetics: a new paradigm for advanced photocatalytic technologies

A multiscale-designed optofluidic reactor is demonstrated in this work, featuring an overall reaction rate constant of 1.32 s¯¹ for photocatalytic decolourization of methylene blue, which is an order of magnitude higher as compared to literature records. A novel performance-enhancement mechanism of microscale in situ photogalvanic acceleration was found to be the main reason for the superior optofluidic performance in the photocatalytic degradation of dyes as a model reaction

Topological properties and fractal analysis of recurrence network constructed from fractional Brownian motions

Many studies have shown that we can gain additional information on time series by investigating their accompanying complex networks. In this work, we investigate the fundamental topological and fractal properties of recurrence networks constructed from fractional Brownian motions (FBMs). First, our results indicate that the constructed recurrence networks have exponential degree distributions; the relationship between $H$ and $$can be represented by a cubic polynomial function. We next focus on the motif rank distribution of recurrence networks, so that we can better understand networks at the local structure level. We find the interesting superfamily phenomenon, i.e. the recurrence networks with the same motif rank pattern being grouped into two superfamilies. Last, we numerically analyze the fractal and multifractal properties of recurrence networks. We find that the average fractal dimension$$ of recurrence networks decreases with the Hurst index $H$ of the associated FBMs, and their dependence approximately satisfies the linear formula $\approx 2 - H$. Moreover, our numerical results of multifractal analysis show that the multifractality exists in these recurrence networks, and the multifractality of these networks becomes stronger at first and then weaker when the Hurst index of the associated time series becomes larger from 0.4 to 0.95. In particular, the recurrence network with the Hurst index $H=0.5$ possess the strongest multifractality. In addition, the dependence relationships of the average information dimension $$and the average correlation dimension$$ on the Hurst index $H$ can also be fitted well with linear functions. Our results strongly suggest that the recurrence network inherits the basic characteristic and the fractal nature of the associated FBM series.Comment: 25 pages, 1 table, 15 figures. accepted by Phys. Rev.

Exploiting Cognitive Structure for Adaptive Learning

Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to satisfy the unique needs of each learner. Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e.g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items. To fully exploit the multifaceted cognitive structure for learning path recommendation, we propose a Cognitive Structure Enhanced framework for Adaptive Learning, named CSEAL. By viewing path recommendation as a Markov Decision Process and applying an actor-critic algorithm, CSEAL can sequentially identify the right learning items to different learners. Specifically, we first utilize a recurrent neural network to trace the evolving knowledge levels of learners at each learning step. Then, we design a navigation algorithm on the knowledge structure to ensure the logicality of learning paths, which reduces the search space in the decision process. Finally, the actor-critic algorithm is used to determine what to learn next and whose parameters are dynamically updated along the learning path. Extensive experiments on real-world data demonstrate the effectiveness and robustness of CSEAL.Comment: Accepted by KDD 2019 Research Track. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19