NATURAL GAS OF NEW GENETIC TYPE——BIO-, THERMO-CATALYTIC TRANSITIONAL ZONE GAS

Bio-, thermo-catalytic transitional zone gas (BTCTZ gas), of which burial depth is shallow (1000-2500m), chemical composition (C,/ΣC1-5 = 0.7-0.9) and isotopic composition (δ13C1= -55 to-48‰) coincide with the evolution level of organic matter (R0=0.25-0.6%) in the corresponding strata, is a natural gas of new genetic type. The genesis of BTCTZ gas must have some relation to microcatalysis under lower temperature and condensation of aromatic hydrocarbons. In line with the new theory of natural gas formation, the existence of BTCTZ gas is objective. According to data from all over the world, BTCTZ gas may hold an important position in the table of reserves of natural gas.     

A Markov Model of Production, Trade, and Money: Theory and Artificial Life Simulation

The paper generalizes the Kiyotaki-Wright trade model by treatingthe trading period as a finite game, so Nash's theorem can be used to provethe existence of equilibrium, and by treating the economy as a Markovprocess, so an ergodic theorem can be used to show the existence ofequilibria with desirable properties (e.g., in which money exists). A Markovmodel of trade also allows us to add complexity to the economy withoutadding corresponding complexity to the analysis of the model's properties.The paper also provides artificial life simulations of the Markov economysuggesting that monetary equilibria are dynamically stable and do notrequire high levels of learning or information processing on the part ofagents.     

基于灰关联分析的GA-BP神经网络在汽车油耗估算中的应用

从设计参数特征入手分析影响汽车油耗的因素,利用灰关联分析方法,解析了各设计参数对汽车油耗的影响程度,选择其中灰关联度较大的设计参数作为输入数据,综合工况油耗作为输出数据,构建6-5-1层结构的BP神经网络预测模型,并利用遗传算法获得优化后的BP神经网络的权值和阈值,然后训练BP神经网络得到最优值,最后以国内市场340款汽车作为研究样本,进行有效性验证.研究结果表明,模型利用灰关联分析获得影响汽车油耗的主要因素,简化了网络结构;与优化前的BP神经网络相比,具有更高的预测精度和可靠性.     

Adjustment of Residual Stresses in Unsymmetric Fiber-Reinforced Composites Using Genetic Algorithms

During the manufacturing process of multilayered fiber-reinforced composites with variable fiber orientations, residual stresses build up in these composites due to the directional expansion of single unidirectionally reinforced layers. Depending on the laminate lay-up, the inhomogeneous residual stresses, which are caused by thermal effects, moisture absorption, and chemical shrinkage, can lead to large multistable out-of-plane deformations. Instead of avoiding these curvatures, they can be advantageously used for technical applications following the near-net-shape technology. In order to adjust the deformations to the technical requirements, genetic algorithms in combination with a nonlinear calculation method have been developed, which can purposefully adapt the laminate lay-up depending on the loading and process parameters.     

Oscillatory and anti-oscillatory motifs in genetic regulatory networks

Recently,self-sustained oscillatory genetic regulatory networks(GRNs) have attracted significant attention in the biological field.Given a GRN,it is important to anticipate whether the network could generate oscillation with proper parameters,and what the key ingredients for the oscillation are.In this paper the ranges of some function-related parameters which are favorable to sustained oscillations are considered.In particular,some oscillatory motifs appearing with high-frequency in most of the oscillatory GRNs are observed.Moreover,there are some anti-oscillatory motifs which have a strong oscillation repressing effect.Some conclusions analyzing these motif effects and constructing oscillatory GRNs are provided.     

SWEIA艾滋病毒传染模型及应用

人类免疫缺陷病毒(HIV)是一种严重威胁生命的病毒,感染艾滋病毒患者一般经历四个阶段:i)艾滋病毒阴性的窗口期(W);ii)阳性的无症状潜伏期(E);iii)有症状期(Ⅰ);以及iv)移除阶段(A).为深入研究艾滋病传播过程,建立SWEIA艾滋病毒传染模型,定义基本再生数,分析无病与地方病平衡点的存在性和局部稳定性,根据2004至2015年中国艾滋病患者数据,采用遗传算法对SWEIA模型中参数进行估计.通过对基本再生数敏感性分析以及模型数值随参数不同而产生的变化,揭示艾滋病窗口期的接触率是影响艾滋病流行的主要原因之一.     

Textile porosity determination based on a nonlinear heat and moisture transfer model

The inverse problems of textile materials design on heat and moisture transfer properties are important and indispensable in applications in the body-clothing-environment system. We present an inverse problem of textile porosity determination (IPTPD) based on a nonlinear heat and moisture transfer model. Adopting the idea of the least-squares, the mathematical formulation of IPTPD is deduced to a regularized optimization problem with collocation method applied. The continuity of the regularized minimization problem is proved. By means of genetic algorithm (GA), the approximate solution of the IPTPD is numerically obtained. To reduce the computational cost, an improved algorithm based on BP neural network with GA is proposed in the numerical simulation. Compared with the direct GA searching, the computational cost is greatly reduced, which presents a similar result.     

一种基于遗传算法的肽/蛋白质结合模式虚拟筛选建模技术

“合理”QSAR模型是指在了解配体与受体相互作用模式的前提下建立定量构效关系, 这样避开了传统做法仅仅依靠样本集分子自身信息来构建预测模型的诸多弊端. 本文将此思想应用于肽/蛋白质亲和活性的研究当中, 借助于遗传算法作为虚拟受体结合靶点及相互作用模式的筛选手段得到了一种新的建模技术: 肽/蛋白质结合模式遗传虚拟筛选(genetic virtual screening of combinative mode for peptide/protein, GVSC). 该法成功解决了“合理”QSAR研究中的难题, 即大多数情况下受体结构未知而难以了解配基与之发生的结合方式. 分别使用58个血管紧张素转化酶, 18个Camel抗体蛋白cAb-lys3双位点突变残基对GVSC加以检验, 其结果表明GVSC能够较好地阐明配基与受体之间的作用机理, 并能得到优于传统方法的QSAR模型.     

Is Genetic Engineering a Route to Enhance Microalgae-Mediated Bioremediation of Heavy Metal-Containing Effluents?

Contamination of the biosphere by heavy metals has been rising, due to accelerated anthropogenic activities, and is nowadays, a matter of serious global concern. Removal of such inorganic pollutants from aquatic environments via biological processes has earned great popularity, for its cost-effectiveness and high efficiency, compared to conventional physicochemical methods. Among candidate organisms, microalgae offer several competitive advantages; phycoremediation has even been claimed as the next generation of wastewater treatment technologies. Furthermore, integration of microalgae-mediated wastewater treatment and bioenergy production adds favorably to the economic feasibility of the former process—with energy security coming along with environmental sustainability. However, poor biomass productivity under abiotic stress conditions has hindered the large-scale deployment of microalgae. Recent advances encompassing molecular tools for genome editing, together with the advent of multiomics technologies and computational approaches, have permitted the design of tailor-made microalgal cell factories, which encompass multiple beneficial traits, while circumventing those associated with the bioaccumulation of unfavorable chemicals. Previous studies unfolded several routes through which genetic engineering-mediated improvements appear feasible (encompassing sequestration/uptake capacity and specificity for heavy metals); they can be categorized as metal transportation, chelation, or biotransformation, with regulation of metal- and oxidative stress response, as well as cell surface engineering playing a crucial role therein. This review covers the state-of-the-art metal stress mitigation mechanisms prevalent in microalgae, and discusses putative and tested metabolic engineering approaches, aimed at further improvement of those biological processes. Finally, current research gaps and future prospects arising from use of transgenic microalgae for heavy metal phycoremediation are reviewed.