GPS掩星技术和电离层反演

系统地介绍了GPS无线电掩星技术的发展现状、系统组成和一些关键技术；论述了利用GPS掩星数据进行电离层反演的理论、模型及相应的计算流程．并结合GPS／MET和CHAMP卫星的实测资料，计算了电子密度剖面，与用其他方法所得结果的对比表明．GPS掩星电离层观测具有精度高、覆盖范围大等特点；最后讨论了GPS掩星技术应用于地震前兆监测的机理和前景。     

德州市深层地下水人工回灌试验浅探

德州市德城区由于长期开采深层地下水,引起了水位区域性大幅度下降,形成了大面积地下水降落漏斗,并产生了地面沉降等不良地质环境问题。为保护德州市地质环境和深层地下水资源,于2002年开展了深层地下水人工回灌试验。根据回灌试验成果和深层地下水目前开采强度推算,利用德城区现有部分(65%)深机井在丰水季节进行人工回灌,单井回灌量保持1200m3/d,每年保持90d的回灌时间,回灌量可达到1080×104m3/a,可增可采资源量675×104m3/a。因此开展人工回灌可以有效地补给地下水源,解决深层地下水资源枯竭问题。     

滑坡变形预测灰色神经网络耦合模型的构建及适用性分析

结合灰色模型和神经网络的数据处理特点,提出串联、并联和混联式3种结构的灰色神经网络滑坡变形预测模型。串联式将滑坡变形位移时序分解为趋势项和随机项,采用灰色模型提取滑坡位移时序趋势,利用神经网络逼近随机波动;并联式以灰色模型和神经网络分别对滑坡预测,采用智能非线性组合,按照预测目标精度动态调整权重,从而获取最终组合预测结果;混联式通过增加灰白化层及灰模型群,对神经网络拓扑结构进行优化,达到弱化滑坡原始监测数据随机性、提高预测模型稳健性的目的。将3种模型应用于古树屋滑坡变形预测,并对其适用性进行讨论。结果表明,3种结构的灰色神经网络耦合模型均提高了预测精度,适用于复杂状况下滑坡体的变形预测。     

Hybrids between olive flounder Paralichthys olivaceus and stone flounder Kareius bicoloratus: karyotype, allozyme and RAPD analyses

The hybrid between olive flounder Paralichthys olivaceus and stone flounder Kareius bicoloratus was produced by artificial insemination of olive flounder eggs with stone flounder Sperm. Sinistral and dextral are two types of hybrid progeny after metamorphosis. Karyotypes of both hybrid flounders are the same as those of the two parental species. Of the 22 loci examined from 12 allozymes,12 confirmed hybridization of the paternal and matemal loci in hybrids and no difference was found in allozyme patterns of sinistral and dextral hybrid fishes. RAPD patterns of these specimens were also studied with 38 primers selected from 104 tested. Among them, the PCR products of 30 primers showed hybridization of the paternal and maternal bands. Genetic variation between hybrids and their parental stocks was analyzed by RAPD using 10 of the above 38 primers. The average heterozygosity and genetic distance were calculated. The results suggested that the filial generation could inherit a little more genetic materials from paternal fish than that from maternal fish.     

Ecosystem health assessment of Honghu Lake Wetland of China using artificial neural network approach

Honghu Lake, located in the southeast of Hubei Province, China, has suffered a severe disturbance during the past few decades. To restore the ecosystem, the Honghu Lake Wetland Protection and Restoration Demonstration Project (HLWPRDP) has been implemented since 2004. A back propagation (BP) artificial neural network (ANN) approach was applied to evaluatinig the ecosystem health of the Honghu Lake wetland. And the effectiveness of the HLWPRDP was also assessed by comparing the ecosystem health before and after the project. Particularly, 12 ecosystem health indices were used as evaluation parameters to establish a set of three-layer BP ANNs. The output is one layer of ecosystem health index. After training and testing the BP ANNs, an optimal model of BP ANNs was selected to assess the ecosystem health of the Honghu Lake wetland. The result indicates that four stages can be identified based on the change of the ecosystem health from 1990 to 2008 and the ecosystem health index ranges from morbidity before the implementation of HLWPRDP (in 2002) to middle health after the implementation of the HLWPRDP (in 2005). It demonstrates that the HLWPRDP is effective and the BP ANN could be used as a tool for the assessment of ecosystem health.     

SIMULATION AND PREDICTION OF DEBRIS FLOW USING ARTIFICIAL NEURAL NETWORK

Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural haz-ard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting de-bris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and use-fill in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time se-ries of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collect-ed in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed.     

CLOBAL CHARM OF THE CHANGJIANG RIVER DELTA

1SPLENDOROFHISTORICALCULTURE InChina,theChangjiang(Yangtze)RiverDelta①gen-erallymeansanadvancedproductivityandculture,whichaffectsdeeplynotonlythewholeChinabutal-sotheAsian-Pacificregion,evenEuropeandAfrica. 1.1CradlefortheCommunicationandPropagationofCivilization ThedevelopmentcourseoftheChangjiangRiverDelta'scultureisdevious,buttheNeolithicHemuduCultureexcavatedinYuyaoCityofZhejiangProvinceindicatestheadvancedpaddyriceculture;MajiabangCulture5000aB.P.andEastBridgeRuin…     

水污染物浓度的神经网络预测模式及效果检验

污染物浓度预测是环境保护的重要内容,将神经网络用于水中有机污染物浓度的预测并对效果进行检验.结果表明,预测值与观测值符合较好.     

形变大地测量学的进展、问题与地震预报

简要概括了形变大地测量学的革命性进展，研讨了它的科学特色、功能和定义以及对地球科学和防灾减灾的推动。形变大地测量学有助于从根本上击破多年来制约地震预报的“瓶颈”，但也存在不少急待解决的问题。着重研讨了在21世纪前10年，形变大地测量学如何依托多年的学科积累并充分受益于人造卫星和数字化等新技术，开展创新性研究和试验以推进地震预报。为此，对当前的研究工作提出了12条科学技术途径。最后对学科名称提出了建议。     

向心扩张指数构建及其在郑州都市圈城镇空间扩张中的应用

探究城镇空间扩张的方向性及其时间变化特征,能够反映都市圈发展过程中核心城市与边缘地区空间关联的动态过程,为制定都市圈发展的相关政策提供科学依据。本文从都市圈的核心城市与边缘地区相互关联的视角,借鉴物理学中力的分解原理,构建了同时兼顾扩张强度和方向性的向心扩张指数CEI,并以郑州都市圈为对象,采用2000—2018年30 m分辨率的全球人造不透水面数据产品GAIA,分析了建设用地扩张的总体数量和占比变化、动态度及强度变化、以及空间方向性变化等特征,进一步应用多元逻辑回归模型,揭示了影响城镇空间扩张向心性的关键因素。结果表明：(1)向心扩张指数CEI为揭示都市圈核心城市对边缘地区的辐射带动作用提供方法支撑,能够定量刻画边缘地区朝向核心城市的发展趋势;(2)在郑州都市圈近20年的快速扩张过程中,尽管各区县市的DEI指数和CEI指数随时间而增减波动,但郑州都市圈总体呈现“核心—边缘”发展模式,除个别地级市中心城区及其下辖县以外,多数区县市的向心性显著且随时间增强,郑州市中心城区对于边缘地区具有显著的辐射带动作用;(3)核心城市与边缘地区的人均GDP差异及城镇化率差异是影响边缘地区向心扩张的重要...