Flexizyme‐Mediated Genetic Reprogramming As a Tool for Noncanonical Peptide Synthesis and Drug Discovery

Noncanonical peptides occur frequently in Nature, and often display high bioactivity. However, the lack of tractable systems for the synthesis of diverse libraries of such peptides has thus far hampered their development as drugs. Genetic reprogramming techniques, in which noncanonical amino acids may be incorporated into peptides, have largely removed this limitation. This Concept article outlines the development of these techniques with an emphasis on drug discovery.     

Properties of a genetic algorithm extended by a random self-learning operator and asymmetric mutations: A convergence study for a task of powder-pattern indexing

Genetic algorithms represent a powerful global-optimisation tool applicable in solving tasks of high complexity in science, technology, medicine, communication, etc. The usual genetic-algorithm calculation scheme is extended here by introduction of a quadratic self-learning operator, which performs a partial local search for randomly selected representatives of the population. This operator is aimed as a minor deterministic contribution to the (stochastic) genetic search. The population representing the trial solutions is split into two equal subpopulations allowed to exhibit different mutation rates (so called asymmetric mutation). The convergence is studied in detail exploiting a crystallographic-test example of indexing of powder diffraction data of orthorhombic lithium copper oxide, varying such parameters as mutation rates and the learning rate. It is shown through the averaged (over the subpopulation) fitness behaviour, how the genetic diversity in the population depends on the mutation rate of the given subpopulation. Conditions and algorithm parameter values favourable for convergence in the framework of proposed approach are discussed using the results for the mentioned example. Further data are studied with a somewhat modified algorithm using periodically varying mutation rates and a problem-specific operator. The chance of finding the global optimum and the convergence speed are observed to be strongly influenced by the effective mutation level and on the self-learning level. The optimal values of these two parameters are about 6 and 5%, respectively. The periodic changes of mutation rate are found to improve the explorative abilities of the algorithm. The results of the study confirm that the applied methodology leads to improvement of the classical genetic algorithm and, therefore, it is expected to be helpful in constructing of algorithms permitting to solve similar tasks of higher complexity.     

大功率光纤放大器分段泵浦方式下的理论分析

利用数值算法计算了掺镱双包层光纤放大器分段泵浦方式下的稳态速率方程组,分析比较了双端泵浦和多段泵浦方式下的最佳光纤长度,最高温度和效率,并采用遗传算法对分布泵浦功率的大小和每段光纤长度同时进行了优化。结果表明,通过优化,大功率分段泵浦光纤放大器获得了较为平坦的温度分布,与双端泵浦相比,大大降低了最高工作温度,斜率效率略有下降。     

基于遗传算法的指纹图像分割算法

在指纹技术中,指纹图像分割是图像处理中关键的一步。将遗传算法用于计算指纹图像分割阈值,采用染色体编码的方法并利用赌轮法产生初始化种群,通过选择算子、交叉算子和变异算子的方法实现分割。该方法表明利用遗传算法所得到的阈值进行分割处理能取得较好的效果。     

Efficient inverse radiation analysis in a cylindrical geometry using a combined method of hybrid genetic algorithm and finite-difference Newton method

An inverse radiation problem was considered to estimate boundary conditions such as temperature distribution and emissivity in axisymmetric absorbing, emitting, and scattering medium, given the measured incident radiative heat fluxes. The finite-volume method was employed to solve a direct radiative transfer equation for a two-dimensional axisymmetric geometry. Various parameter estimators, such as conjugate-gradient method, hybrid genetic algorithm, and finite-difference Newton method, were employed to solve the inverse problems, while discussing their performances in terms of estimation accuracy and computational efficiency. Based on this, we proposed, as a best inverse analysis tool, a new combined method that adopted the hybrid genetic algorithm as an initial value selector and used the finite-difference Newton method as a parameter estimator.     

Four-dimensional parameter estimation of plane waves using swarming intelligence

This paper proposes an efficient approach for four-dimensional(4D) parameter estimation of plane waves impinging on a 2-L shape array. The 4D parameters include amplitude, frequency and the two-dimensional(2D) direction of arrival,namely, azimuth and elevation angles. The proposed approach is based on memetic computation, in which the global optimizer, particle swarm optimization is hybridized with a rapid local search technique, pattern search. For this purpose,a new multi-objective fitness function is used. This fitness function is the combination of mean square error and the correlation between the normalized desired and estimated vectors. The proposed hybrid scheme is not only compared with individual performances of particle swarm optimization and pattern search, but also with the performance of the hybrid genetic algorithm and that of the traditional approach. A large number of Monte–Carlo simulations are carried out to validate the performance of the proposed scheme. It gives promising results in terms of estimation accuracy, convergence rate, proximity effect and robustness against noise.     

能量采集型无线传感器网络的高能效路由算法

在能量采集型无线传感器网络中,虽然有能量吸收,但是因能量依然非常珍贵,如何优化路由协议,提高能量利用率,延长网络寿命仍然是值得研究的问题.为求解高能效的路由,提出了一种采用遗传算法的高能效路由算法,建立考虑节点的吸收能量、剩余能量、消耗能量和浪费能量的适应函数,用遗传算法寻找全局最优路径.将该适应函数与3种其他适应函数作对比,其他3种适应函数分别为只考虑路径能耗最小的适应函数,考虑路径能耗与路径上节点的吸收能量、剩余能量的适应函数以及考虑路径能耗与网络中所有节点的浪费能量的适应函数.采用遗传算法解出4种路由,通过仿真分析可知,所提出的路由算法能量利用效率最高.     

低温下JB-9014钝感炸药DSD参数研究

爆轰冲击波动力学（Detonation Shock Dynamics,DSD）是目前研究爆轰波非理想传播的有效途径。利用DSD的广义几何光学模型,研究了大长径比药柱中爆轰波非理想传播现象,根据-30 ℃下直径为10～30 mm药柱的直径效应实验数据,利用遗传算法确定了低温下JB-9014钝感炸药的DSD参数。由DSD参数计算得到了JB-9014药柱中的定态波形和爆速,计算结果与实验结果符合。