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1.
Kunde Yang Yuanliang Ma Chao Sun Miller J.H. Potty G.R. 《Oceanic Engineering, IEEE Journal of》2004,29(4):964-972
This paper discusses the results of geoacoustic inversion carried out using explosive charge data from the Asian Seas International Acoustic Experiment (ASIAEX) East China Sea (ECS) Experiment. A multifrequency incoherent matched-field inversion processor and a genetic algorithm (GA) are used for the inversion. A multistep matched field inversion approach is presented, which makes use of the varying sensitivities of wave fields at various frequencies to reduce the inversion problem into a sequence of smaller inversions with fewer unknowns to estimate at each stage. Different parameters are estimated using data at different frequencies according to their sensitivities. Inversion results for different areas in the ECS region are summarized and compared with core data. 相似文献
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培养基对海藻真菌PT2菌株的生长及活性代谢产物合成的影响 总被引:3,自引:0,他引:3
本文采用PD、GPY、YES、SYE、GPM5种不同培养基培养海藻真菌菌株PT2,并对其在不同培养基中的生长情况、代谢产物的形成及抗肿瘤活性进行分析,旨在了解不同培养基对菌株生长和活性物质合成的影响.结果显示,PT2菌株在不同培养基上的培养特征及生长速率有所不同,液体培养基发酵液抽提物的抗肿瘤活性也有较大差别,其中SYE发酵液抽提物活性最强,对KB、Raji和Hela 3种肿瘤细胞的IC50分别为7.6μg/cm^3、2.4μg/cm^3和3.3μg/cm^3.采用HPLC法对不同抽提物进行分析,发现它们之间的组成成分有显著差异.本文的实验结果表明,不同培养基对该菌株的生长及活性代谢产物的合成具有显著的影响。 相似文献
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A one-dimensional model for thinning of the plasma sheet is developed on the basis of launching a fast mode MHD rarefaction wave propagating in the tailward direction along the plasma sheet. Behind the rarefaction wave the pressure is reduced, leading to thinning of the plasma sheet and also to an Earthward plasma flow with a speed on the order of the sound speed a0. The plasma sheet thickness is reduced by a factor of 2 if an Earthward plasma flow speed of 0.8a0 is induced. The predictions of the model are in reasonable agreement with observations. 相似文献
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在灰岩地区钻探施工过程中,常见孔壁坍塌、掉块现象,尤其在含泥晶灰岩地层分布区钻进过程中,这种现象尤为突出,轻则影响钻探施工效率,重则造成钻探孔内事故。针对孔内坍塌、掉块现象的征兆及特点,提出了相对应的处理办法。 相似文献
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Debris flows have caused serious loss of human lives and a lot of damage to properties in Taiwan over the past decades. Moreover, debris flows have brought massive mud causing water pollution in reservoirs and resulted in water shortage for daily life locally and affected agricultural irrigation and industrial usages seriously. A number of methods for prediction of debris flows have been studied. However, the successful prediction ratio of debris flows cannot always maintain a stable and reliable level. The objective of this study is to present a stable and reliable analytical model for occurrence predictions of debris flows. This study proposes an Artificial Neural Networks (ANN) model that was constructed by seven significant factors using back-propagation (BP) algorithm. These seven factors include (1) length of creek, (2) average slope, (3) effective watershed area, (4) shape coefficient, (5) median size of soil grain, (6) effective cumulative rainfall, and (7) effective rainfall intensity. A total of 178 potential cases of debris flows collected in eastern Taiwan were fed into the ANN model for training and testing. The average ratio of successful prediction reaching 93.82% demonstrates that the presented ANN model with seven significant factors can provide a stable and reliable result for the prediction of debris flows in hazard mitigation and guarding systems. 相似文献
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