基于遥感监测的黄海绿潮漂移路径及分布面积特征分析

本文利用2008～2012年的多源卫星遥感序列图像,动态监测各年绿潮发生的漂移路径、影响海域面积,得到各年绿潮漂移趋势、分布面积和最大分布面积出现时间。根据监测结果,划分出影响绿潮漂移路径的关键区域,并分析了2008、2009和2010年关键区域内各年绿潮漂移方向和面积变化情况。     

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。     

焦化废水对蚕豆和大麦毒性的影响

以COD作为主要参照指标,研究了焦化废水在符合GB 13456-92<钢铁工业水污染物排放标准>焦化一级、二级排放标准限值要求时,对蚕豆和大麦幼根生长、根尖细胞遗传毒性的影响.结果表明:在实验周期内,焦化废水对蚕豆幼根根长、根重和有丝分裂指数的影响不大;对大麦幼根根重无明显影响,而对大麦根长和有丝分裂指数有促进作用.焦...     

路面径流水环境下交通承载力分析

为了对路面径流水容许污染总量控制下的交通承载力问题进行探讨,利用神经网络具有的非线性映射能力和遗传算法具有的全局随机搜索能力,结合公路路面径流水质检测数据,提出了一种基于遗传神经网络进行公路交通环境承载力反计算的分析方法,应用该方法可根据路面径流水质污染数据反演出路段交通量大小,并可据此进行交通量与路而径流水质污染的关...     

生态恢复过程中的种群遗传学考虑

生态恢复是生态科学的最终实验,从种群角度看,恢复的目标是使种群（尤其是优势种或建群种）到具有生长、繁殖和适应进化变化的能力。要达到这个目标,种群遗传学知识必不可少。遗传变异是物种适应变化环境的基础,而局部适应则是种群适应局部环境遗传分化的结果,它们在生态恢复中起着重要的作用。分析了生态恢复过程中影响种群遗传变异因素,主要有取样误差和小种群效应（主要是瓶颈或建立者事件及其以后的近交和漂变等）,在进行     

Gradient analysis of the chrogenetic structure of lowland and highland scotch pine populations

Gradients of genetic distances (GGDs) between 26 adjacent cenopopulations of Scotch pine (Pinus sylvestris L.) along the transects through the distinct landscape-ecotopic isolation borders were analyzed graphically. The results made it possible to reveal and quantitatively estimate gradients and borders of gene-pool structural patterns in populations of this species in the central part of Northern Eurasia. In lowland areas, the most distinct borders were found between pine populations growing on dry lands and bogs (Pineta sphagnosa) in the forest zone of the Transural region, as well as between the Arakaragaiskii and Amankaragaiskii island pine forests in the steppe zone (Northern Turgai). In highland areas (the Urals and the Carpathians), the greatest GGDs were observed between low-mountain (about 600 m above sea level) and middle-mountain (850–900 m) populations. Analysis of GGDs is a promising gene-geographic method for determining population borders and studying the chorogenetic structure of species.     

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%.     

AHP与GA-SVM耦合模型在滑坡预警中的应用*

为了减少滑坡造成的损失,提高滑坡预测的准确性,通过搭建灾害模拟平台获得滑坡的实验数据,在获得多组模拟实验数据后,分析各变量的特性。首先,通过层次分析(Analytic Hierarchy Process,AHP)算法,对滑坡进行危险度划分;然后,通过支持向量机(Support Vector Machine,SVM)建立模型,遗传算法(Genetic Algorithm,GA)再优化SVM参数,提出1种层次分析法与GA-SVM相耦合的模型。研究结果表明:AHP方法划分后的数据,通过GA与SVM结合建立的模型精度较好,实验预测结果与实际结果较为吻合,与单一SVM相比,精度更高,结果更好,更加适用于多变量的复杂非线性滑坡预警。     

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.     

Post-flooding disaster crop diversity recovery: a case study of Cowpea in Mozambique

To restore food security to a traditional African cropping system following a sudden loss of seed, genetic diversity must be re-established. This study examines the extent to which Cowpea diversity was reinstated two years after a flood disaster in Gaza Province, Mozambique. The contribution that seed from various sources made to the recovery was assessed using semi-structured interviews and morphological and molecular data. Data suggest that diversity had recovered to some extent yet there was evidence of a narrowing of the genetic base, with fewer rare alleles and differences in the distribution of allele frequencies. Although the main channels for accessing seed after the flood were seed relief and markets, these sources contributed to minimal and different diversity. It appears that diversity was regained primarily through social networking in the form of loans or gifts of seed from friends and relatives. The results of the study are discussed in relation to seed relief approaches.