2-氨基-4H-咪唑啉-4-酮衍生物的快速平行合成法

咪唑啉酮衍生物是一类具有良好生物活性和药理活性的杂环化合物 [1,2 ] ,尤其是一些 2 -氨基咪唑啉酮表现出良好的杀菌、抗炎及抗癌活性 [3 ,4 ] .从自然界如一些海洋生物中可分离得到含 2 -氨基咪唑啉酮结构的生物碱 [5,6] .最近 ,组合化学方法广泛地应用于有机合成 ,它包括固相合成法和液相合成法[7～ 9] .我们曾应用氮杂 Wittig反应制得 2 -氨基取代咪唑啉酮衍生物 ,部分化合物表现出一定的抑菌活性 [10 ,11] .本文进一步报道应用液相平行反应法快速合成 2 -氨基 - 4H-咪唑啉 - 4-酮衍生物 (4) .该方法应用烯基膦亚胺 1与苯基异氰酸酯的…     

一步法高通量可控制备生物相容水/水微囊及其响应释放

生物相容水/水微囊在药物递送、 医学治疗等领域具有重要应用. 本文通过设计同轴微流控器件, 结合数值模拟优化和流动阻力分析, 实现一步法高通量可控制备大小均匀、 尺寸可控、 壁厚可调、 生物相容的水/水微囊. 采用实验研究和数值模拟相结合的方式, 研究了微流控器件结构、 内相流速、 外相流速、 外相/空气界面张力、 内相/外相界面张力、 内相黏度和外相黏度等参数对水/水微囊直径和壁厚的调控规律. 通过微通道流动阻力分析, 设计多通道平行放大微流控器件, 实现尺寸均匀可控水/水微囊的高通量制备. 验证了生物相容水/水微囊作为活性物质的理想载体, 可以通过改变pH或溶解囊壁释放载体, 进而实现活性物质的pH响应释放, 为其实际应用奠定了基础.     

TWO-SCALE FINITE ELEMENT DISCRETIZATIONS FOR PARTIAL DIFFERENTIAL EQUATIONS

Some two-scale finite element discretizations are introduced for a class of linear partialdifferential equations. Both boundary value and eigenvalue problems are studied. Basedon the two-scale error resolution techniques, several two-scale finite element algorithmsare proposed and analyzed. It is shown that this type of two-scale algorithms not onlysignificantly reduces the number of degrees of freedom but also produces very accurateapproximations.     

光纤法珀应变传感器串并联混合复用的离散腔长变换解调研究

光纤法珀传感器复用的输出信号复杂,解调相当困难.为实现其串并联混合结构复用,根据光纤法珀应变传感器的双光干涉原理,阐述了传感器串并联混合复用的输出信号特征,分析了信号中各项成分对离散腔长变换方法解调结果的影响,并进行了计算机仿真.最后搭建实验平台,进行了四个传感器串并联混合复用的实验研究,证明该方案可行,解调准确度1 μm.     

An Edge Server Placement Method Based on Reinforcement Learning

In mobile edge computing systems, the edge server placement problem is mainly tackled as a multi-objective optimization problem and solved with mixed integer programming, heuristic or meta-heuristic algorithms, etc. These methods, however, have profound defect implications such as poor scalability, local optimal solutions, and parameter tuning difficulties. To overcome these defects, we propose a novel edge server placement algorithm based on deep q-network and reinforcement learning, dubbed DQN-ESPA, which can achieve optimal placements without relying on previous placement experience. In DQN-ESPA, the edge server placement problem is modeled as a Markov decision process, which is formalized with the state space, action space and reward function, and it is subsequently solved using a reinforcement learning algorithm. Experimental results using real datasets from Shanghai Telecom show that DQN-ESPA outperforms state-of-the-art algorithms such as simulated annealing placement algorithm (SAPA), Top-K placement algorithm (TKPA), K-Means placement algorithm (KMPA), and random placement algorithm (RPA). In particular, with a comprehensive consideration of access delay and workload balance, DQN-ESPA achieves up to 13.40% and 15.54% better placement performance for 100 and 300 edge servers respectively.     

A minimization version of a directed subgraph homeomorphism problem

We consider a special case of the directed subgraph homeomorphism or topological minor problem, where the host graph has a specific regular structure. Given an acyclic directed pattern graph, we are looking for a host graph of minimal height which still allows for an embedding. This problem has applications in compiler design for certain coarse-grain reconfigurable architectures. In this application domain, the task is to simultaneously schedule, bind and route a so-called data-flow graph, where vertices represent operations and arcs stand for data dependencies between the operations, given an orthogonal grid structure of reconfigurable processing elements (PEs) that have restricted communication abilities. We show that the problem of simultaneously scheduling, binding and routing is NP-complete by describing a logic engine reduction from NAE-3-SAT. This result holds even when the input graph is a directed tree with maximum indegree two. We also give a |V|^3/2-approximation algorithm. J. A. Brenner’s research supported by the DFG Research Center Matheon “Mathematics for key technologies”. J. C. van der Veen’s research supported by DFG Focus Program 1148, “Reconfigurable Architectures”, Grants FE 407/8-1 and FE 407/8-2.     

Topological structure preserving numerical simulations of dynamical models

This paper brings together two methods producing numerical solutions with a statement of their quality — the nonstandard finite difference method and the method of validated computing. It deals with the construction and the analysis of reliable numerical discretizations of dynamical systems by employing these two techniques. An epidemiological model is used as a model example for their combined application.     

Parallel preconditioned conjugate gradient algorithm on GPU

We propose a parallel implementation of the Preconditioned Conjugate Gradient algorithm on a GPU platform. The preconditioning matrix is an approximate inverse derived from the SSOR preconditioner. Used through sparse matrix–vector multiplication, the proposed preconditioner is well suited for the massively parallel GPU architecture. As compared to CPU implementation of the conjugate gradient algorithm, our GPU preconditioned conjugate gradient implementation is up to 10 times faster (8 times faster at worst).     

Coupled Navier–Stokes—Molecular dynamics simulations using a multi‐physics flow simulation framework

Simulation of nano‐scale channel flows using a coupled Navier–Stokes/Molecular Dynamics (MD) method is presented. The flow cases serve as examples of the application of a multi‐physics computational framework put forward in this work. The framework employs a set of (partially) overlapping sub‐domains in which different levels of physical modelling are used to describe the flow. This way, numerical simulations based on the Navier–Stokes equations can be extended to flows in which the continuum and/or Newtonian flow assumptions break down in regions of the domain, by locally increasing the level of detail in the model. Then, the use of multiple levels of physical modelling can reduce the overall computational cost for a given level of fidelity. The present work describes the structure of a parallel computational framework for such simulations, including details of a Navier–Stokes/MD coupling, the convergence behaviour of coupled simulations as well as the parallel implementation. For the cases considered here, micro‐scale MD problems are constructed to provide viscous stresses for the Navier–Stokes equations. The first problem is the planar Poiseuille flow, for which the viscous fluxes on each cell face in the finite‐volume discretization are evaluated using MD. The second example deals with fully developed three‐dimensional channel flow, with molecular level modelling of the shear stresses in a group of cells in the domain corners. An important aspect in using shear stresses evaluated with MD in Navier–Stokes simulations is the scatter in the data due to the sampling of a finite ensemble over a limited interval. In the coupled simulations, this prevents the convergence of the system in terms of the reduction of the norm of the residual vector of the finite‐volume discretization of the macro‐domain. Solutions to this problem are discussed in the present work, along with an analysis of the effect of number of realizations and sample duration. The averaging of the apparent viscosity for each cell face, i.e. the ratio of the shear stress predicted from MD and the imposed velocity gradient, over a number of macro‐scale time steps is shown to be a simple but effective method to reach a good level of convergence of the coupled system. Finally, the parallel efficiency of the developed method is demonstrated. Copyright © 2009 John Wiley & Sons, Ltd.