A systematic study of magnetic field in Relativistic Heavy-ion Collisions in the RHIC and LHC energy regions

The features of magnetic field in relativistic heavy-ion collisions are systematically studied by using a modified magnetic field model in this paper. The features of magnetic field distributions in the central point are studied in the RHIC and LHC energy regions. We also predict the feature of magnetic fields at LHC $\sqrt{s_{NN}}$= 900, 2760 and 7000 GeV based on the detailed study at RHIC $\sqrt{s_{NN}}$ = 62.4, 130 and 200 GeV. The dependencies of the features of magnetic fields on the collision energies, centralities and collision time are systematically investigated, respectively.Comment: 8 pages, 7 figure

Well-posedness and Long-time Behavior of a Bulk-surface Coupled Cahn-Hilliard-diffusion System with Singular Potential for Lipid Raft Formation

We study a bulk-surface coupled system that describes the processes of lipid-phase separation and lipid-cholesterol interaction on cell membranes, in which cholesterol exchange between cytosol and cell membrane is also incorporated. The PDE system consists of a surface Cahn-Hilliard equation for the relative concentration of saturated/unsaturated lipids and a surface diffusion-reaction equation for the cholesterol concentration on the membrane, together with a diffusion equation for the cytosolic cholesterol concentration in the bulk. The detailed coupling between bulk and surface evolutions is characterized by a mass exchange term $q$. For the system with a physically relevant singular potential, we first prove the existence, uniqueness and regularity of global weak solutions to the full bulk-surface coupled system under suitable assumptions on the initial data and the mass exchange term $q$. Next, we investigate the large cytosolic diffusion limit that gives a reduction of the full bulk-surface coupled system to a system of surface equations with non-local contributions. Afterwards, we study the long-time behavior of global solutions in two categories, i.e., the equilibrium and non-equilibrium models according to different choices of the mass exchange term $q$. For the full bulk-surface coupled system with a decreasing total free energy, we prove that every global weak solution converges to a single equilibrium as $t\to +\infty$. For the reduced surface system with a mass exchange term of reaction type, we establish the existence of a global attractor

Collective Flow Distributions and Nuclear Stopping in Heavy-ion Collisions at AGS, SPS and RHIC

We study the production of proton, antiproton and net-proton at \AGS, \SPS and \RHIC within the framework non-uniform flow model(NUFM) in this paper. It is found that the system of RHIC has stronger longitudinally non-uniform feature than AGS and SPS, which means that nuclei at RHIC energy region is much more transparent. The NUFM model provides a very good description of all proton rapidity at whole AGS, SPS and RHIC. It is shown that our analysis relates closely to the study of nuclear stopping and longitudinally non-uniform flow distribution of experiment. This comparison with AGS and SPS help us to understand the feature of particle stopping of thermal freeze-out at RHIC experiment.Comment: 16 pages,7 figure

Determination of oleanolic and ursolic acids in different parts of Perilla frutescens by high-performance liquid chromatography

Perilla frutescens (L.) Britt.(Lamiaceae), a famous traditional Chinese medicine, has been used for the treatment of various diseases. To evaluate the quality of P. frutescens, a simple, rapid and accurate high-performance liquid chromatography (HPLC) method was developed for the assessment of two bioactive triterpene acids: oleanolic acid (OA) and ursolic acid (UA). The HPLC system used an Kromasil 100 C18 RP column with methanol and aqueous H3PO4 as the mobile phase and detection at 210 nm. The method was precise with relative standard deviations for these two constituents that ranged between 0.3-0.6 % (intraday) and 0.6-1.2 % (interday). The contents of the OA and UA in P. frutescens were determined with recoveries ranging from 96.7 to 102.0%. The content of these two phytochemicals in different parts of P. frutescens growing at five different locations of China were determined to establish the effectiveness of the method

A Unified Object Counting Network with Object Occupation Prior

The counting task, which plays a fundamental role in numerous applications (e.g., crowd counting, traffic statistics), aims to predict the number of objects with various densities. Existing object counting tasks are designed for a single object class. However, it is inevitable to encounter newly coming data with new classes in our real world. We name this scenario as \textit{evolving object counting}. In this paper, we build the first evolving object counting dataset and propose a unified object counting network as the first attempt to address this task. The proposed model consists of two key components: a class-agnostic mask module and a class-incremental module. The class-agnostic mask module learns generic object occupation prior via predicting a class-agnostic binary mask (e.g., 1 denotes there exists an object at the considering position in an image and 0 otherwise). The class-incremental module is used to handle new coming classes and provides discriminative class guidance for density map prediction. The combined outputs of class-agnostic mask module and image feature extractor are used to predict the final density map. When new classes come, we first add new neural nodes into the last regression and classification layers of class-incremental module. Then, instead of retraining the model from scratch, we utilize knowledge distillation to help the model remember what have already learned about previous object classes. We also employ a support sample bank to store a small number of typical training samples of each class, which are used to prevent the model from forgetting key information of old data. With this design, our model can efficiently and effectively adapt to new coming classes while keeping good performance on already seen data without large-scale retraining. Extensive experiments on the collected dataset demonstrate the favorable performance.Comment: Under review; The dataset and code will be available at: https://github.com/Tanyjiang/EOC