Advances in Artificial Cell Membrane Systems as a Platform for Reconstituting Ion Channels

An artificial cell membrane that is composed of bilayer lipid membranes (BLMs) with transmembrane proteins incorporated within them represents a well‐defined system for the analysis of membrane proteins, especially ion channel proteins that are major targets for drug design. Because the BLM system has a high compatibility with recently developed cell‐free expression systems, it has attracted attention as a next‐generation drug screening system. However, three issues associated with BLM systems, i. e., their instability, the need for non‐volatile organic solvents and a low efficiency of ion channel incorporation, have limited their use as a drug screening platform. In this personal account, we discuss our recent approaches to address these issues based on microfabrication. We also discuss the potential for using the BLM system combined with cell‐free expression systems as a drug screening system for future personalized medicine.     

A data-driven neural network approach to optimal asset allocation for target based defined contribution pension plans

A data-driven Neural Network (NN) optimization framework is proposed to determine optimal asset allocation during the accumulation phase of a defined contribution pension scheme. In contrast to parametric model based solutions computed by a partial differential equation approach, the proposed computational framework can scale to high dimensional multi-asset problems. More importantly, the proposed approach can determine the optimal NN control directly from market returns, without assuming a particular parametric model for the return process. We validate the proposed NN learning solution by comparing the NN control to the optimal control determined by solution of the Hamilton–Jacobi–Bellman (HJB) equation. The HJB equation solution is based on a double exponential jump model calibrated to the historical market data. The NN control achieves nearly optimal performance. An alternative data-driven approach (without the need of a parametric model) is based on using the historic bootstrap resampling data sets. Robustness is checked by training with a blocksize different from the test data. In both two and three asset cases, we compare performance of the NN controls directly learned from the market return sample paths and demonstrate that they always significantly outperform constant proportion strategies.     

Algebraic signatures of convex and non-convex codes

A convex code is a binary code generated by the pattern of intersections of a collection of open convex sets in some Euclidean space. Convex codes are relevant to neuroscience as they arise from the activity of neurons that have convex receptive fields. In this paper, we develop algebraic methods to determine if a code is convex. Specifically, we use the neural ideal of a code, which is a generalization of the Stanley–Reisner ideal. Using the neural ideal together with its standard generating set, the canonical form, we provide algebraic signatures of certain families of codes that are non-convex. We connect these signatures to the precise conditions on the arrangement of sets that prevent the codes from being convex. Finally, we also provide algebraic signatures for some families of codes that are convex, including the class of intersection-complete codes. These results allow us to detect convexity and non-convexity in a variety of situations, and point to some interesting open questions.     

Willmore flow of planar networks

Geometric gradient flows for elastic energies of Willmore type play an important role in mathematics and in many applications. The evolution of elastic curves has been studied in detail both for closed as well as for open curves. Although elastic flows for networks also have many interesting features, they have not been studied so far from the point of view of mathematical analysis. So far it was not even clear what are appropriate boundary conditions at junctions. In this paper we give a well-posedness result for Willmore flow of networks in different geometric settings and hence lay a foundation for further mathematical analysis. A main point in the proof is to check whether different proposed boundary conditions lead to a well posed problem. In this context one has to check the Lopatinskii–Shapiro condition in order to apply the Solonnikov theory for linear parabolic systems in Hölder spaces which is needed in a fixed point argument. We also show that the solution we get is unique in a purely geometric sense.     

基于WSN分布式聚类均衡路由算法的优化研究

有效减少网络能耗,从而提高整体网络寿命是无线传感网领域中的关键问题,LEACH和DEBR等分簇路由算法可以初步提高网络寿命和网络延展性.研究了簇头选择、簇的形成和数据路由3个阶段,提出了一种基于能量均衡的分布式聚类均衡路由算法,通过传感节点的剩余能量、邻居个数以及簇头能耗的混合权重来选择簇头和分簇,考虑每条路径的消耗来选择最佳路径,有效改进了LEACH随机选择簇头节点和DEBR传送延迟导致的网络分割和能耗不均等问题.仿真结果表明,该算法有效平衡并降低了节点能耗,使得网络中生存节点数在相同周期内有较大提升,从而延长了整个网络的生命周期.     

非线性系统神经网络控制器 BP 算法的改进

对非线性系统神经网络控制器提出了一种改进的误差反向传播算法。该算法引入一维参数寻优法,预估神经网络连接权系数学习步长,减少了步长选取的盲目性,避免了学习过程陷入局部极小的可能性,并以二自由度机器人为对象,进行了神经网络控制的仿真研究。     

一种用PROLOG实现非精确推理的方法

本文通过对模糊f-Horn子句的语义和推理机制的分析,提出了一种直接用Horn子句做非精确推理方法,并用Turbo Prolog编制了实例程序,对该方法做了检验。证明这种方法对于直接用Prolog语言编制需要做非精确推理的人工智能软件是可行的。     

并行单元归结

给出了基于神经网络的单元归结算法.首先将子句集S表示为δ形,并且用算子对(⊙, )引入两种类型的神经元,然后用这两种神经元构造子句集S的神经网络结构,而后给出基于Horn子句集的神经网络的归结算法,最后证明了该算法的完备性,并用实例进行了验证.     

基于优化博弈算法在WMN信道分配中的应用

针对无线Mesh网在资源分配公平性方面存在的不足,提出了一种基于损耗因子动态优化权重的博弈算法,算法将博弈论融合在无线Mesh网的信道分配问题中,把节点间的竞争看作博弈参与者,采取博弈策略来竞争信道资源.针对信道分配过程中存在的损耗,引入损耗因子来动态更新博弈权重,使博弈能够按需索取,达到信道公平分配的目的.实验结果表明,改进算法使得网络吞吐量有所提升,并且保证了信道分配的相对公平.     

人工培育青石斑鱼Epinephelus awoara仔、稚、幼鱼的饵料系列

本文研究人工培育青石斑鱼仔、稚、幼鱼的饲料条件。青石斑鱼从仔鱼－稚鱼－幼鱼的各个发育阶段均为动物食性。自初孵仔鱼至幼鱼发育过程大抵有三个饵料转换期：（１）前期仔鱼到后期仔鱼由内源性营养（卵黄、油球）到外源性营养（以小型浮游动物为主）的转换期。（２）后期仔鱼末（２５日龄左右、全长９毫米以上）到稚鱼期系由吃小型浮游动物到吃大型浮游动物转换期。（３）稚鱼后期、幼鱼阶段（约４０日龄以后、全长２０毫米以上）是向底栖饵料（鱼、虾、贝肉）转换期。用牡蛎受精卵和幼体作青石斑鱼仔鱼开口饵料。其饵料系列是：牡蛎受精卵和幼体－轮虫－桡足类、卤虫无节幼体、卤虫成体、糠虾－鱼、虾贝肉。以初孵仔鱼２～３万尾／米￣３、稚鱼０．１～０．２万尾／米￣３、幼鱼０．０５万尾／米￣３的培苗密度，各种饵料的日投饵量范围：牡蛎受精卵和幼体１－３０个／ｍｌ，轮虫０．２－１８个／ｍｌ，桡足类０．１个／ｍｌ，卤虫幼体０．１－３．５个／ｍｌ，卤虫成体１－１０克／米￣３，糠虾１－１４０克／米￣３，鱼、虾、贝肉５－１２２克／米￣３。