A Survey of the Spatio-Temporal Data Model

Traditional GIS(Geographic Information System)mostly can only describe the transient state data and does not have the ability to deal with the temporal dynamic data. When the data changes, new data will take the place of the old data, namely the transient state change into another, and the old data will disappear. Therefore, it is unable to make an analysis of the updating changes of the data and predict the development trend of the future. In this case, TGIS (Temporal Geographic Information System) emerges and expands the time dimension on the basis of traditional GIS. Spatio-temporal data model is the key to TGIS. Spatio-temporal data modeling is not only related to dynamic expression of spatio-temporal objects, but also gives an important support for spatio-temporal analysis and reasoning. This paper summarized the theories and applications status at home and abroad of spatio-temporal data model in detail, illustrated family tree of spatio-temporal data model for the nearly fifty years, discussed improvement and application status of Base State with Amendments Model, Event-based Spatio-temporal Data Model, Object-oriented Data Model and other spatio-temporal data models, and raised the existing problems of spatio-temporal data model. The current existing problems mainly includes: ①There are a lot of spatio-temporal data models put forward, but some of them only focus on semantic design and neglect the verified; ②Most of existing spatio-temporal data model are for vector data, only the Event-based Spatio-temporal Data Model is raster data structure; ③At present, the expression the time-space information of geographic entity is relative separated with spatio-temporal data model; ④Spatio-temporal data model is mainly used in cadastral management, land use and forestry data updating, less application in other fields. In the end, the future development direction was put forward of spatio-temporal data model. In the time of big data and “Internet plus”, it is necessary to explore the big data spatio-temporal data model that supports multiple data formats.     

白缘(鱼央)5S rDNA的扩增分析及其在染色体上的FISH定位

本文分别对雌雄白缘(鱼央)的5S rDNA进行了PCR扩增和序列分析,并采用双色荧光原位杂交(FISH)技术,以白缘(鱼央)的5S rDNA序列和小麦的45S rDNA为探针,对其在白缘(鱼央)雌雄中期染色体上进行了FISH定位研究。结果表明,白缘(鱼央)5S rDNA序列无雌雄差异;5S rDNA的保守区序列为117 bp;5S rDNA和45S rDNA分别被定位于白缘(鱼央)的性染色体和第5号染色体上。同时从GenBank中获得了22种鱼的5S rDNA,运用DNAman软件构建了23种鱼的系统发育树,对白缘(鱼央)的进化地位进行了初步研究。本研究为阐明白缘(鱼央)在鱼类系统进化中的位置、重复序列在脊椎动物性染色体上的分布状况以及与性别决定与分化的关系,提供了资料积累和分析依据。     

基于18S和ITS-5.8S rDNA基因序列的白色霞水母(Cyanea nozakii)的分子鉴定与检测

采用通用引物PCR扩增法,测定了辽东湾海域的白色霞水母(Cyanea nozakii)螅状体、碟状体及水母体的18S以及ITS-5.8S r DNA序列,同时利用Gene Bank数据库中已有同源序列对其进行序列分析及系统分析。结果显示,白色霞水母3个个体的18S和ITS-5.8S r DNA序列完全一致。白色霞水母样品的ITS-5.8S r DNA序列与Gen Bank中未知真核生物的序列高度相似(≥99%),推测该物种可能是早期发育阶段(卵、浮浪幼虫或碟状体)的白色霞水母样品。霞水母属不同种间18S r DNA序列经比对后同源序列长度为1709bp,多态位点数33个;比对后ITS1同源序列长度为368bp,其中变异位点203个,简约信息位点数178个,单变异位点21个。基于18S r DNA基因序列的霞水母属种内和种间平均遗传距离分别为0、0.008,而基于ITS1序列的霞水母属种内和种间平均遗传距离分别为0.019、0.284。基于ITS1的种间遗传距离是种内遗传距离的15倍,适合于进行物种鉴定。NJ系统树的结果也表明同种的不同个体各自聚枝,其聚类结果大致与形态分类吻合。研究表明,ITS基因片段在霞水母不同种间变异较大,更适于大型水母种类鉴定、检测及属内种间水平的系统进化研究。     

春季我国不同纬度河口浮游动物群落变化趋势

根据2010-2012年春季由南向北河口海域：北仑河口、九龙江口、瓯江口、长江口和灌河口的浮游动物调查资料;通过比较不同纬度河口浮游动物群落的生物多样性指数（H''）、物种更替率（R）和群落相似系数（S）;探讨了河口浮游动物群落特征沿着不同纬度变化的趋势。结果表明;自南向北5个河口可以划分为3个不同的浮游动物区系。北仑河口和九龙江口为南部亚热带区系;位于亚热带海域;以亚热带种为主;H''分别为1.01和2.18;两河口之间R值为65.52%;S值为51.28%;瓯江口和长江口为中部过渡区系;位于亚热带和温带过渡海域;亚热带种和暖温带种是主要出现的种类;H''分别为1.31和1.86;两河口之间R值为58.33%;S值为58.82%;灌河口单独为北部温带区系;位于温带海域;暖温带种是主要出现的种类;H''值为1.19;与其余河口物种更替率均高于80.00%;群落相似系数均低于32.00%;表明群落结构由南到北的变化非常显著。线性回归结果表明：我国不同河口之间浮游动物的物种更替率与纬度差（两河口所处纬度的差值;δN）呈正相关关系（R=0.026ΔN+0.608;p=0.002）;群落相似系数与纬度差呈负相关关系（S=0.034ΔN+0.578;p=0.001）。春季;我国不同纬度河口浮游动物群落结构的变化;主要是因为影响各河口的水温和水团存在较大差别所致。     

Longleaf Pine (Pinus palustris Mill.) Morphology and Climate/Growth Responses Along a Physiographic Gradient in North Carolina

Geographic differences in tree morphology and climate and growth responses of longleaf pine have been documented, yet how these differences vary at a larger scale has not. In this study, we documented changes in tree morphology and climate and radial growth responses of six longleaf pine (Pinus palustris Mill.) stands in three physiographic regions in North Carolina. We sampled from more than fifteen trees per stand and compared site- and regional-level total and latewood ring width values to temperature, precipitation, and drought. All morphological characteristics expressed a strong west–east gradient. Climate and radial growth response was strongest for the Sandhills region and then Piedmont and Coastal Plain regions. The distinct morphological characteristic gradient did not covary with climate and radial growth response, suggesting that additional environmental influences affect needle length, trunk diameter, and height.     

Prediction of Seaward Slope Recession in Berm Breakwaters Using M5' Machine Learning Approach

In the design process of berm breakwaters, their front slope recession has an inevitable rule in large number of model tests, and this parameter being studied. This research draws its data from Moghim’s and Shekari’s experiment results. These experiments consist of two different 2D model tests in two wave flumes, in which the berm recession to different sea state and structural parameters have been studied. Irregular waves with a JONSWAP spectrum were used in both test series. A total of 412 test results were used to cover the impact of sea state conditions such as wave height, wave period, storm duration and water depth at the toe of the structure, and structural parameters such as berm elevation from still water level, berm width and stone diameter on berm recession parameters. In this paper, a new set of equations for berm recession is derived using the M5'' model tree as a machine learning approach. A comparison is made between the estimations by the new formula and the formulae recently given by other researchers to show the preference of new M5'' approach.     

基于梯度提升算法的岩性识别方法

传统的岩性识别方法如岩屑录井、钻井取心及测井资料解释等技术,对录井质量的依赖程度较高,识别精度与效率低,泛化能力差。随着计算机技术的迅速发展,将测井资料与计算机技术相结合开展岩性研究已成为岩性识别的有效手段。本文提出了一种基于梯度提升算法XGBoost和LightGBM的岩性识别方法。以苏里格气田苏东41-33区块下碳酸盐岩储层为例进行测试验证,采用该方法结合测井资料中的声波时差、自然伽马、光电吸收截面指数、密度、深侧向电阻率和补偿中子等6种参数进行岩性识别,并与KNN （K近邻分类器）、朴素贝叶斯和支持向量机等传统算法进行对比,结果表明,3种传统算法的岩性识别准确率分别为78.45%、74.43%和78.72%,基于梯度提升算法XGBoost和LightGBM的识别准确率分别达到了98.90%和98.72%,远高于传统算法。     

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments,but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT)model and the K-means cluster algorithm to produce a regional landslide susceptibility map.Yanchang County,a typical landslide-prone area located in northwestern China,was taken as the area of interest to introduce the proposed application procedure.A landslide inventory containing 82 landslides was prepared and subse-quently randomly partitioned into two subsets:training data(70％landslide pixels)and validation data(30％landslide pixels).Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means clus-ter algorithm.The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC)curve)of the proposed model was the highest,reaching 0.88,compared with traditional models(support vector machine(SVM)=0.85,Bayesian network(BN)=0.81,frequency ratio(FR)=0.75,weight of evidence(WOE)=0.76).The landslide frequency ratio and fre-quency density of the high susceptibility zones were 6.76/km2 and 0.88/km2,respectively,which were much higher than those of the low susceptibility zones.The top 20％interval of landslide occurrence probability contained 89％of the historical landslides but only accounted for 10.3％of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without contain-ing more"stable"pixels.Therefore,the obtained susceptibility map is suitable for application to landslide risk management practices.     

基于四叉树的LOD地形简化及几何变形纠正

论述了一种基于四叉树结构LOD地形快速简化及几何变形纠正方法。通过建立四叉树结构将地形划分为规则的多个小块,利用一个递归过程遍历四叉树结构。在判断过程中本文将地形粗糙度因素加入到节点评价系统当中,有效地实现了地形的简化。对漫游过程中出现的几何变形现象引入一个混合因子到节点评价系统中,较好地解决了几何变形问题。为了更真实地反映地物地貌,本文将具有较高分辨率的航空影像叠加到DEM地形模型上,取得了较好的效果。     

Molecular phylogenetic analysis of sulfate-reducing bacteria from deep sediment layers of the tropical West Pacific warm pool

1IntroductionThe tropical West Pacific warm pool(TWP-WP),which spans an area roughly between10°Nto10°S of the equator from Indonesia to the dateline,has the world’s warmest sea surface temperature ofbeing greater than29℃.With the increase of recog-niz…