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  • Hindawi Limited  (3)
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  • Hindawi Limited  (3)
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  • 1
    In: Geofluids, Hindawi Limited, Vol. 2022 ( 2022-9-25), p. 1-7
    Abstract: Marine vertical cable seismic (VCS) collects seismic waves by hydrophone array vertically suspended in seawater to prospect the offshore geological structure and monitor the reservoir. Due to its irregular source-receiver geometry, the primary imaging has narrow illustration coverage. Here, we proposed a cross-correlation transformation based on ghost wave interferometry. This method can transform the ghost reflections from the vertical cable seismic profile into the virtual surface seismic primaries just like those excited by the source and recorded by marine seismic towed-streamer below sea surface. After processing these virtual primaries with conventional method, we can obtain the ghost reflection imaging section with high resolution which effectively extend the illustration footprints in the subsurface. By application of this transform, virtual primaries are generated from the first-order ghost reflections of the actual VCS data. Then, migration of these virtual primaries provides a high-resolution image of hydrate-bearing sediments.
    Type of Medium: Online Resource
    ISSN: 1468-8123 , 1468-8115
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2045012-6
    SSG: 13
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  • 2
    In: BioMed Research International, Hindawi Limited, Vol. 2014 ( 2014), p. 1-5
    Abstract: Background . Cancer is a significant disease burden in the world. Many studies showed that heavy metals or their compounds had connection with cancer. But the data conflicting about the relationship of manganese (Mn) to cancer are not enough. In this paper, the relationship was discussed between Mn concentrations in drinking water for rural residents and incidence and mortality caused by malignant tumors in Huai’an city. Methods . A total of 158 water samples from 28 villages of 14 towns were, respectively, collected during periods of high flow and low flow in 3 counties of Huai’an city, along Chinese Huai’he River. The samples of deep groundwater, shallow groundwater, and surface water were simultaneously collected in all selected villages. Mn concentrations in all water samples were determined by inductively coupled plasma-mass spectrometry (ICP-MS 7500a). The correlation analysis was used to study the relationship between the Mn concentration and cancer incidence and mortality. Results . Mn concentrations detectable rate was 100% in all water samples. The mean concentration was 452.32  μ g/L ± 507.76  μ g/L. There was significant difference between the high flow period and low flow period ( t = - 5.23 , P 〈 0.05 ) and also among deep groundwater, shallow groundwater, and surface water ( F = 5.02 , P 〈 0.05 ). The ratio of superscale of Mn was 75.32%. There was significant difference of Mn level between samples in the high flow period and low flow period ( χ 2 = 45.62 ,   P 〈 0.05 ) and also among deep groundwater, shallow groundwater, and surface water ( χ 2 = 10.66 , P 〈 0.05 ). And also we found that, during the low flow period, Mn concentration has positive correlation with cancer incidence and mortality; for a 1  μ g/L increase in Mn concentration, there was a corresponding increase of 0.45/100000 new cancer cases and 0.35/100000 cancer deaths ( P 〈 0.05 ). Conclusions . In Huai’an city, the mean concentration of Mn in drinking water was very high. Mn concentration correlated with cancer incidence and mortality.
    Type of Medium: Online Resource
    ISSN: 2314-6133 , 2314-6141
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2014
    detail.hit.zdb_id: 2698540-8
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  • 3
    Online Resource
    Online Resource
    Hindawi Limited ; 2014
    In:  Mathematical Problems in Engineering Vol. 2014 ( 2014), p. 1-14
    In: Mathematical Problems in Engineering, Hindawi Limited, Vol. 2014 ( 2014), p. 1-14
    Abstract: Extension neural network (ENN) is a new neural network that is a combination of extension theory and artificial neural network (ANN). The learning algorithm of ENN is based on supervised learning algorithm. One of important issues in the field of classification and recognition of ENN is how to achieve the best possible classifier with a small number of labeled training data. Training data selection is an effective approach to solve this issue. In this work, in order to improve the supervised learning performance and expand the engineering application range of ENN, we use a novel data selection method based on shadowed sets to refine the training data set of ENN. Firstly, we use clustering algorithm to label the data and induce shadowed sets. Then, in the framework of shadowed sets, the samples located around each cluster centers (core data) and the borders between clusters (boundary data) are selected as training data. Lastly, we use selected data to train ENN. Compared with traditional ENN, the proposed improved ENN (IENN) has a better performance. Moreover, IENN is independent of the supervised learning algorithms and initial labeled data. Experimental results verify the effectiveness and applicability of our proposed work.
    Type of Medium: Online Resource
    ISSN: 1024-123X , 1563-5147
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2014
    detail.hit.zdb_id: 2014442-8
    SSG: 11
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