Genetic regulatory network-based symbiotic evolution

The main theme of this paper is to present a novel evolution, the genetic regulatory network-based symbiotic evolution (GRNSE), to improve the convergent speed and solution accuracy of genetic algorithms. The proposed GRNSE utilizes genetic regulatory network (GRN) reinforcement learning to improve the diversity and symbiotic evolution (SE) initialization to achieve the parallelism. In particular, GRN-based learning increases the global rate by regulating members of genes in symbiotic evolution. To compare the efficiency of the proposed method, we adopt 41 benchmarks that contain many nonlinear and complex optimal problems. The influences of dimension, individual population size, and gene population size are examined. A new control parameter, the population rate is introduced to initiate the ratio between the gene and chromosome. Finally, all the studies of there 41 benchmarks demonstrate that from the statistic point of view, GRNSE give a better convergence speed and a more accurate optimal solution than GA and SE.     

Swarm intelligence-based extremum seeking control

This paper proposes an extremum seeking control (ESC) scheme based on particle swarm optimization (PSO). In the proposed scheme, the controller steers the system states to the optimal point based on the measurement, and the explicit form of the performance function is not needed. By measuring the performance function value online, a sequence, generated by PSO algorithm, guides the regulator that drives the state of system approaching to the set point that optimizes the performance. We also propose an algorithm that first reshuffles the sequence, and then inserts intermediate states into the sequence, in order to reduce the regulator gain and oscillation induced by population-based stochastic searching algorithms. The convergence of the scheme is guaranteed by the PSO algorithm and state regulation. Simulation examples demonstrate the effectiveness and robustness of the proposed scheme.     

A belief-rule-based inventory control method under nonstationary and uncertain demand

This paper is devoted to investigating inventory control problems under nonstationary and uncertain demand. A belief-rule-based inventory control (BRB-IC) method is developed, which can be applied in situations where demand and demand-forecast-error (DFE) do not follow certain stochastic distribution and forecasting demand is given in single-point or interval styles. The method can assist decision-making through a belief-rule structure that can be constructed, initialized and adjusted using both manager’s knowledge and operational data. An extended optimal base stock (EOBS) policy is proved for initializing the belief-rule-base (BRB), and a BRB-IC inference approach with interval inputs is proposed. A numerical example and a case study are examined to demonstrate potential applications of the BRB-IC method. These studies show that the belief-rule-based expert system is flexible and valid for inventory control. The case study also shows that the BRB-IC method can compensate DFE by training BRB using historical demand data for generating reliable ordering policy.     

SFFS-PC-NN optimized by genetic algorithm for dynamic prediction of financial distress with longitudinal data streams

Recently, research of financial distress prediction has become increasingly urgent. However, existing static models for financial distress prediction are not able to adapt to the situation that the sample data flows constantly with the lapse of time. Financial distress prediction with static models does not meet the demand of the dynamic nature of business operations. This article explores the theoretical and empirical research of dynamic modeling on financial distress prediction with longitudinal data streams from the view of individual enterprise. Based on enterprise’s longitudinal data streams, dynamic financial distress prediction model is constructed by integrating financial indicator selection by using sequential floating forward selection method, dynamic evaluation of enterprise’s financial situation by using principal component analysis at each longitudinal time point, and dynamic prediction of financial distress by using back-propagation neural network optimized by genetic algorithm. This model’s ex-ante prediction efficiently combines its ex-post evaluation. In empirical study, three listed companies’ half-year longitudinal data streams are used as the sample set. Results of dynamic financial distress prediction show that the longitudinal and dynamic model of enterprise’s financial distress prediction is more effective and feasible than static model.     

基于Virtools的三维交互虚拟精馏实验室的构建

为了实现对精馏分离实验过程的三维情景化交互仿真模拟,提出了一种基于Virtools技术的、以虚拟人物为引导的,全场景、开放式、三维沉浸交互式虚拟精馏实验室的构建方法。该软件以板式塔实验为考察对象,依据精馏分离理论建立了分离预测数学模型并以此为构建基础,通过对功能模块、实现的关键技术以及三维沉浸感提升方法的研究与设计,采用编写及优化程序实现了物系选择、多参数调节、监控评价和色谱检测等功能,计算了不同条件下沿塔气液组成和温度分布,完整地模拟了精馏分离和馏出液检测全过程。     

H.264帧内预测模式选择快速算法的研究

提出一种适用于H.264帧内预测的快速算法,利用相邻像素间的梯度筛选预测模式来避免不必要的预测模式计算。实验结果表明:用全I帧编码,该算法在图像质量和输出码率基本不变的情况下,编码时间大约节省了60%。     

Dimensional precision simulation of the formed picture tube panel

In the forming process of picture tube panel, the accumulated residual stresses cause the formed part to shrink, and the thermal and mechanical loads cause the mold blocks to deform. These two factors result in large deviations on the dimensions of the formed panel, which are both modeled and simulated in this paper. For residual stresses analysis, a thermo-rheologically simple viscoelastic material model is introduced to consider the stresses relaxation effect and to describe the mechanical behavior according to the temperature change. The shrinkage of formed parts induced by the residual stresses is calculated based on the theory of shells, represented as an assembly of flat elements formed by combining the constant strain and the discrete Kirchhoff triangular elements. A thermoelastic model is presented to predict the deformation of the mold blocks during pressing, which is based on the steady mold temperature field and thermoelastic boundary element method. The integrated simulation results suggest the amounts that the mold cavity should be machined by, and have been verified by comparing the dimensional precision of the panels produced by the mold considering a uniform part shrinkage and mold expansion or the mold considering the predicted ununiform part shrinkage and mold deformation.     

Proteomic examination of Leishmania chagasi plasma membrane proteins: Contrast between avirulent and virulent (metacyclic) parasite forms

Purpose: About two million new cases of leishmaniasis with 50 000 associated deaths occur worldwide each year. Promastigotes of the causative Leishmania spp. develop from the procyclic stage to the highly virulent metacyclic stage within the sand fly vector. We hypothesized that proteins important for promastigote virulence might be uniquely represented in the plasma membrane of metacyclic, but not procyclic, promastigotes. Experimental design: Procyclic (logarithmic) promastigotes and purified metacyclic promastigotes from stationary phase cultures of Leishmania chagasi were used to prepare membrane preparations either by surface biotinylation‐streptavidin affinity separation or by octyl glucoside detergent extraction. Results: These membrane fractions were enriched over 130‐ and 250‐fold, respectively, as estimated by Western blotting for the plasma membrane's major surface protease. Hundreds or dozens of proteins were identified by LC‐MS/MS in the surface biotinylation or detergent extraction, respectively. Confocal microscopy suggested the difference between the lists was due to the fact that proteins localized both on the surface membrane and within the flagellar pocket were accessible to surface biotinylation, whereas only proteins on the membrane were obtained by detergent extraction. Using detergent extraction, we found different proteins were present in membranes of the procyclic stage compared to metacyclic stage promastigotes. Several dozen were stage specific. Conclusions and clinical relevance: These data provide a foundation for identifying virulence factors in the plasma membranes of Leishmania spp. promastigotes during metacyclogenesis.     

Performance-First Control for Discrete-Time LQG Problems

Performance-first control for discrete-time LQG is considered in this paper to minimize the probability that the performance index exceeds a preselected threshold via constructing a closed-loop feedback control law. This problem can be converted into a mean-variance control problem which can be solved by developing a nested form of the variance and using polynomial optimization as a solution scheme.