一株焦化废水降酚菌的质粒初步研究

从处理焦化废水的活性污泥池中,分离得到了一株具有降酚能力的细菌, Ｊ Ａ． Ｊ Ａ 可以在含酚的 Ｈ Ｐ 复杂培养基中生长,也可以在以酚为唯一碳源的 Ａ Ｂ Ｐ 培养基中生长,且具有产黑色素的特点．用碱裂解提取质粒的方法发现它含有一个质粒,定名为ｐ Ｘ Ｈ１ ,并测定了这个质粒的大小约为２ ．６ ｋｂ ．用限制性内切酶 Ｂｇｌ Ｉ和 Ｒｓａ Ｉ进行双酶切,作出了初步的物理图谱．ｐ Ｘ Ｈ１ 经过２０ 次没有酚作为选择压传代培养后仍然能１００ ％ 保留,同时, Ｊ Ａ菌仍具有降酚能力,表明其具有很高的遗传稳定性     

遗传和模拟退火算法在水质模型参数确定中的应用简介

水质模型参数的确定是构建水质模型的基础。相对于传统优化方法,现代优化算法的优势日趋明显。本文在基于现代优化算法的水质模型参数确定研究背景下,介绍了具有代表性的遗传算法和模拟退火算法,并对该两种算法的应用现状进行综述,旨在推进该领域的进一步探索。     

基于遗传算法进行EMI滤波器电感的设计

通过电感的高频特性及其对共模插入损耗的影响分析,采用遗传算法和Ngspice仿真库设计出电感的选择方案,最后通过电路仿真和实验同时验证方案的有效性。该方案避免了对EMI滤波器高频段的复杂建模,降低了电感选择的难度,对电感元件的选择具有一定的指导意义。     

西藏自治区措勤县德能铜多金属矿床地质特征及成因分析

通过对西藏自治区措勤县德能铜多金属矿的研究发现,矿区内共6条达到工业品位的铜矿体,并伴生有铅、锌、金、银等多种有益组分,成矿地质条件较好。矿体主要赋存于燕山期石英闪长玢岩岩体外接触带及构造破碎带中,其中Cu4矿体规模最大,经深部钻孔工程及地表槽探验证,矿体品位和厚度沿走向和倾向变化系数较小。燕山晚期第一阶段第一次石英闪长玢岩侵入体是主要成矿物质来源,后期北西西向断裂构造是主要导矿和容矿构造。     

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.     

Predicting land cover using GIS, Bayesian and evolutionary algorithm methods

Modelling land cover change from existing land cover maps is a vital requirement for anyone wishing to understand how the landscape may change in the future. In order to test any land cover change model, existing data must be used. However, often it is not known which data should be applied to the problem, or whether relationships exist within and between complex datasets. Here we have developed and tested a model that applied evolutionary processes to Bayesian networks. The model was developed and tested on a dataset containing land cover information and environmental data, in order to show that decisions about which datasets should be used could be made automatically. Bayesian networks are amenable to evolutionary methods as they can be easily described using a binary string to which crossover and mutation operations can be applied. The method, developed to allow comparison with standard Bayesian network development software, was proved capable of carrying out a rapid and effective search of the space of possible networks in order to find an optimal or near-optimal solution for the selection of datasets that have causal links with one another. Comparison of land cover mapping in the North-East of Scotland was made with a commercial Bayesian software package, with the evolutionary method being shown to provide greater flexibility in its ability to adapt to incorporate/utilise available evidence/knowledge and develop effective and accurate network structures, at the cost of requiring additional computer programming skills. The dataset used to develop the models included GIS-based data taken from the Land Cover for Scotland 1988 (LCS88), Land Capability for Forestry (LCF), Land Capability for Agriculture (LCA), the soil map of Scotland and additional climatic variables.     

Forecasting China’s primary energy demand based on an improved AI model

An improved energy demand forecasting model is built based on the autoregressive distributed lag (ARDL) bounds testing approach and an adaptive genetic algorithm (AGA) to obtain credible energy demand forecasting results. The ARDL bounds analysis is first employed to select the appropriate input variables of the energy demand model. After the existence of a cointegration relationship in the model is confirmed, the AGA is then employed to optimize the coefficients of both linear and quadratic forms with gross domestic product, economic structure, urbanization, and technological progress as the input variables. On the basis of historical annual data from 1985 to 2015, the simulation results indicate that the proposed model has greater accuracy and reliability than conventional optimization methods. The predicted results of the proposed model also demonstrate that China will demand approximately 4.9, 5.6, and 6.1 billion standard tons of coal equivalent in 2020, 2025, and 2030, respectively.     

The ethics of genetic engineering and gene drives in conservation

The ethical issues associated with using genetic engineering and gene drives in conservation are typically described as consisting of risk assessment and management, public engagement and acceptance, opportunity costs, risk and benefit distributions, and oversight. These are important, but the ethical concerns extend beyond them because the use of genetic engineering has the potential to significantly alter the practices, concepts, and value commitments of conservation. I sought to elucidate the broader set of ethical issues connected with a potential genetic engineering turn in conservation and provide an approach to ethical analysis of novel conservation technologies. The primary rationales offered in support of using genetic engineering and gene drives in conservation are efficiency and necessity for achieving conservation goals. The instrumentalist ethical perspective associated with these rationales involves assessing novel technologies as a means to accomplish desired ends. For powerful emerging technologies the instrumentalist perspective needs to be complemented by a form-of-life perspective frequently applied in the philosophy of technology. The form-of-life perspective involves considering how novel technologies restructure the activities into which they are introduced. When the form-of-life perspective is applied to creative genetic engineering in conservation, it brings into focus a set of ethical issues, such as those associated with power, meaning, relationships, and values, that are not captured by the instrumentalist perspective. It also illuminates why the use of gene drives in conservation is so ethically and philosophically interesting.     

基于神经网络和遗传算法的城市火灾风险评价模型

以消防安全工程学与系统安全工程理论为基础,结合我国城市发展特征及消防安全管理状况,建立了城市区域火灾风险评价指标体系;针对神经网络易陷入局部极小而引起评价指标权值分布不合理的缺陷,提出了基于神经网络和遗传算法的城市火灾风险评价模型,该模型以火灾发生的可能性以及灾后的严重程度为输入单元,火灾风险等级为输出单元,采用误差反算法训练BP网络,最终得出火灾风险等级范围,有效地解决了城市火灾的动态性和非线性特征;研究实例证明了该模型的有效性,可为城市的消防安全管理提供确实可行的参考依据。     

铁路危险货物办理站点优化配置方法研究

铁路危险货物运输办理站点的优化配置,涉及到需求分布、设置地点、区域规划、环保要求、业务功能以及与其他运输方式的分工、衔接等,是一个复杂的系统工程。分析了已有铁路危险货物办理站点整合优化模型的不足,首先从铁路部门角度综合考虑现有铁路危险货物办理站点的各方面条件,进行多准则决策评价,从运输需求者角度考虑运输方式多属性决策,进行铁路危险货物办理站点的综合效用评价,并基于投资成本、环境风险、运输效用和区域公平性等方面构建了铁路危险货物办理站点优化配置模型,为了有效解决多站点、多需求、多区域、多目标铁路危险货物办理站点优化配置模型因素复杂、规模大、层次多的问题,提出了运用大系统分解协调的多目标遗传算法求解模型的思路。