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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.
重庆地面最高气温与最大风速年极值的渐近分布   总被引:3,自引:0,他引:3  
利用重庆1951-1990年间地面最高气温和1956-1990年间地面最大风速年极值的记录,采用韦伯分布和耿贝尔分布分别对其渐近分布进行拟合.通过统计推断,找出重庆地面最高气温和地面最大风速年极值遵循的最佳渐近分布--韦伯分布.  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   
6.
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   
7.
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   
8.
提要 本文详细讨论了一种三维重力位场快速正反演方法。作者在前人工作的基础上,对算法作了行之有效的改进,通过对反演中的不稳定因素进行各种理论模型试算,得出保证迭代反演稳定收敛的准则,编制出可在微型机IBM—PC上运行的人机对话式自动正反演程序。本文还对各种不均质模型进行了模似计算并将该方法应用于某含油气沉积盆地的双层界面构造研究,揭示出了储油有利地段。  相似文献   
9.
It has become clear in recent years that relativistic beaming is a good explanation for the BL Lac phenomenon. Of studies based on the relativistic beaming model of BL Lac objects, we note that the orientation of jet's axis to the line-of-sight is very small and, therefore, the observed flux emitted from a rapidly moving source is orders of magnitude higher than the flux in its rest-frame:F obs = 3 + F intr, where is the bulk relativistic Doppler factor. Then the observed apparent magnitudem v must be corrected for this effect. For our 39 samples, the corrected apparent magnitudem v corr and logZ have a good correlation.  相似文献   
10.
在冈底斯岩浆岩带的羊八井地区,林子宗火山岩系上部出现小规模的粗面质火山岩.岩石学与地球化学研究表明粗面岩与时空密切相关的大体积安山质-英安质-流纹质火山岩属于不同的火山岩系:碱性钾质系列与钙碱性系列.详细的地球化学研究证明林子宗晚期小体积钾质岩具有独立的岩浆源区,而非早期钙碱性系统低压岩浆过程演化的产物.粗面质熔岩SiO2含量为62.91%~64.63%,具有高K2O(7.52%~8.05%)、Al2O3(16.64%-17.35%)、低TiO2(0.59%~0.68%)与MsO(0.15%~0.77%).钾质熔岩富集LILE与LREE,亏损HFSE,具有高Rb/Sr(1.1~2.3)、Th/La(0.59~0.80)、Th/U(6.3~10.9)和低Ce/Pb(4.1~6.2)与Ba/Th(4.3~14.5)比值,其同位素组成变化范围分别是87Sr/86Sr(I)=0.7068~0.7075、143Nd/144Nd(I)=0.51241~0.51252、206Pb/204Pb=18.87~18.95、207Pb/204Pb=15.63~15.70和208Pb/204Pb=39.24~39.68.粗面岩的地球化学与同位素特征表明冈底斯新近纪钾质火山岩来源于角闪岩相的消减沉积物熔融.因此,羊八井新近纪钾质火山熔岩提供了印度-亚洲大陆碰撞早期消减沉积物折返的证据.  相似文献   
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