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1.
ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.  相似文献   
2.
ECOLOGICAL SERIES OF SOIL ANIMALS IN DARLIDAI MOUNTAIN   总被引:1,自引:0,他引:1  
The ecological series of soil animals under the broad-leaved and pine mixed forest in Darlidai Mountainwas studied. Seven sample plots were selected according to different altitude gradients, which belong to different vegeta-tion types. By investigating and analyzing soil animals in every sample plot it is found that there are 45 groups and 1956individuals, which axe involved in 3 phylums, 7 classes, 16 orders, respectively. The altitude is a key factor which af-fects ecological series of soil animals. Both the groups and individuals of soil animals increase with altitude increasingunder certain conditions, which contrastes with ordinary cases, resulting from special micro-climate in studied area. Thegroups and individuls of soil animals are the most under the broad-leaved and pine forest on the top of the mountain, andthe least under Picea-Abies forest in the foot of the mountain.  相似文献   
3.
U–Pb age, trace element and Hf isotope compositions of zircon were analysed for a metasedimentary rock and two amphibolites from the Kongling terrane in the northern part of the Yangtze Craton. The zircon shows distinct morphological and chemical characteristics. Most zircon in an amphibolite shows oscillatory zoning, high Th/U and 176Lu/177Hf ratios, high formation temperature, high trace element contents, clear negative Eu anomaly, as well as HREE-enriched patterns, suggesting that it is igneous. The zircon yields a weighted mean 207Pb/206Pb age of 2857 ± 8 Ma, representing the age of the magmatic protolith. The zircon in the other two samples is metamorphic. It has low Th/U ratios, low trace element concentrations, variable HREE contents (33.8 ≥ LuN≥2213; 14.7 ≤ LuN/SmN ≤ 354) and 176Lu/177Hf ratios (0.000030–0.001168). The data indicate that the zircon formed in the presence of garnet and under upper amphibolite facies conditions. The metamorphic zircon yields a weighted mean 207Pb/206Pb age of 2010 ± 13 Ma. These results combined with previously obtained Palaeoproterozoic metamorphic ages suggest a c. 2.0 Ga Palaeoproterozoic collisional event in the Yangtze Craton, which may result from the assembly of the supercontinent Columbia. The zircon in two samples yields weighted mean two-stage Hf model ( T DM2) ages of 3217 ± 110 and 2943 ± 50 Ma, respectively, indicating that their protoliths were mainly derived from Archean crust.  相似文献   
4.
蛤蜊科3种贝类16SrRNA基因片段及ITS2核苷酸序列分析   总被引:4,自引:0,他引:4  
利用PCR技术分别扩增连云港及启东沿海蛤蜊科的西施舌(Coelomactra antiquata)、中国蛤蜊(Mactra chinensis)和四角蛤蜊(Mactra veneriformis)3种双壳贝的16SrRNA基因片段和ITS2核苷酸序列.测序后用DNAstar软件分析了核苷酸差异。结果显示:三种贝类16SrRNA基因片段长度相同,均为306bp(去除引物).核苷酸存在多态性。共有45个变异位点,54个核苷酸发生了变异。全部为碱基置换。西施舌与中国蛤蜊此片段核苷酸的同源性为88.9%.与四角蛤蜊的同源性为88.6%.中国蛤蜊与四角蛤蜊的同源性为90.6%。三种蛤蜊ITS2序列分别为390bp(西施舌)、441bp(四角蛤蜊)和466bp(中国蛤蜊)。存在长度多态性.ITS2核苷酸差异分析结果显示.西施舌与中国蛤蜊的同源性为70.9%-71.1%,西施舌与四角蛤蜊的为70.5%-71.0%。中国蛤蜊与四角蛤蜊的同源性为88.1%-88.8%。ITS2序列分析结果与16SrRNA基因片段分析结果一致.2种分子分析法均显示中国蛤蜊与四角蛤蜊的亲缘关系近。  相似文献   
5.
应用给定换热器结构材料而使换热量最大的两侧换热表面的最佳匹配准则和使可用能损失率最小的最佳运行参数准则。利用两个准则间的关系 ,采用迭代的方式完成换热器的优化设计 ,使换热器的设计达到材料省、换热效果好及运行费用低的目的 ,且能在设计阶段实现。  相似文献   
6.
80年代以来,广西小平阳岩溶区出现了大量的地裂缝。通过调查研究.这些地裂缝的形成是受多种因素控制的.其中包括构造、土体性质、水的作用、地形地貌、气候及植被等因素。在上述因素中.构造是主导因素,上体性质是基础因素.其它则为诱发因素,也是因地质环境要素变化所引起的因素。主导因素是通过诱发因素起作用的.并最终通过土体性质因素导致产土地裂缝。  相似文献   
7.
应用地壳波浪与镶嵌构造学说对富氏谱分析法提取地壳垂直形变信息的科学性做了地质学意义上的阐释 ,并提出了根据多期形变资料提取特定波段上构造策应力的数学模型  相似文献   
8.
The optical flash accompanying GRB 990123 is believed to be powered by the reverse shock of a thin shell. With the best-fit physical parameters for GRB 990123 and the assumption that the parameters in the optical flash are the same as in the afterglow, we show that: 1) the shell is thick rather than thin, and we have provided the light curve for the thick shell case which coincides with the observation; 2) the theoretical peak flux of the optical flash accounts for only 3×10~-4 of the observed. In order to remove this discrepancy, the physical parameters, the electron energy and magnetic ratios, εe and εB, should be 0.61 and 0.39, which are very different from their values for the late afterglow.  相似文献   
9.
大磨曲家金矿位于玲珑金矿田东风矿床主脉的NE延伸部位.矿区内已发现矿脉25条.通过对玲珑金矿田的成矿条件分析和东风矿床的综合研究,认为本区有很好的找矿前景.  相似文献   
10.
The authors analyzed the data collected in the Ecological Station Jiaozhou Bay from May 1991 to November 1994, including 12 seasonal investigations, to determine the characteristics, dynamic cycles and variation trends of the silicate in the bay. The results indicated that the rivers around Jiaozhou Bay provided abundant supply of silicate to the bay. The silicate concentration there depended on river flow variation. The horizontal variation of silicate concentration on the transect showed that the silicate concentration decreased with distance from shorelines. The vertical variation of it showed that silicate sank and deposited on the sea bottom by phytoplankton uptake and death, and zooplankton excretion. In this way, silicon would endlessly be transferred from terrestrial sources to the sea bottom. The silicon took up by phytoplankton and by other biogeochemical processes led to insufficient silicon supply for phytoplankton growth. In this paper, a 2D dynamic model of river flow versus silicate concentration was established by which silicate concentrations of 0.028–0.062 μmol/L in seawater was yielded by inputting certain seasonal unit river flows (m3/s), or in other words, the silicate supply rate; and when the unit river flow was set to zero, meaning no river input, the silicate concentrations were between 0.05–0.69 μmol/L in the bay. In terms of the silicate supply rate, Jiaozhou Bay was divided into three parts. The division shows a given river flow could generate several different silicon levels in corresponding regions, so as to the silicon-limitation levels to the phytoplankton in these regions. Another dynamic model of river flow versus primary production was set up by which the phytoplankton primary production of 5.21–15.55 (mgC/m2·d)/(m3/s) were obtained in our case at unit river flow values via silicate concentration or primary production conversion rate. Similarly, the values of primary production of 121.98–195.33 (mgC/m2·d) were achieved at zero unit river flow condition. A primary production conversion rate reflects the sensitivity to silicon depletion so as to different phytoplankton primary production and silicon requirements by different phytoplankton assemblages in different marine areas. In addition, the authors differentiated two equations (Eqs. 1 and 2) in the models to obtain the river flow variation that determines the silicate concentration variation, and in turn, the variation of primary production. These results proved further that nutrient silicon is a limiting factor for phytoplankton growth. This study was funded by NSFC (No. 40036010), and the Director's Fund of the Beihai Sea Monitoring Center, the State Oceanic Administration.  相似文献   
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