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41.
Biomarkers in the Molar Tooth (MT)-Bearing Limestones in the Jilin-Liaoning Area of China 总被引:2,自引:1,他引:2
The origin of Molar Tooth (MT) carbonates has been argued for more than 100 years, which are a kind of Proterozoic carbonates especially composed of microsparite with ptygmatically folded and sheet-like structures. Biomarkers detected in the microcalcsparite from the Wanlong and Xingmincun formations in the Jilin-Liaoning area showed there are abundant normal alkanes, isoprenoids, hopanes, steranes, alkylmethylcyclohexanes, and alkyleyclohexanes, indicating a diversity of biological source: long-chain isoprenoids, the major components of chlorophyll, such as C19, C20, a kind of major biomarkers synthesized early by isoprenoid monomers; hopanes a type of characteristic biomarkers from prokaryote, such as archaebacteria and cyanobacteria; sterane a biomarker for eukaryote; and two kinds of alkanes with C17, C18 as the main peaks representing aquatic bacteria and with C23, C24 as the main peaks representing fungi, respectively. Biomarker analysis showed that MT is the result of bacterial and algal activities, which is a kind of organisms between aerobic and anaerobic bacteria, reproducing well in normal or slightly saline sea water under weak oxidation-reduction conditions, resulting in rapid deposition of calcite as microsparite clue to some mechanisms. 相似文献
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In this study the inelastic behavior of steel arch bridges subjected to strong ground motions from major earthquakes is investigated by dynamic analyses of a typical steel arch bridge using a three‐dimensional (3D) analytical model, since checking seismic performance against severe earthquakes is not usually performed when designing such kinds of bridge. The bridge considered is an upper‐deck steel arch bridge having a reinforced concrete (RC) deck, steel I‐section girders and steel arch ribs. The input ground motions are accelerograms which are modified ground motions based on the records from the 1995 Hyogoken‐Nanbu earthquake. Both the longitudinal and transverse dynamic characteristics of the bridge are studied by investigation of time‐history responses of the main parameters. It is found that seismic responses are small when subjected to the longitudinal excitation, but significantly large under the transverse ground motion due to plasticization formed in some segments such as arch rib ends and side pier bases where axial force levels are very high. Finally, a seismic performance evaluation method based on the response strain index is proposed for such steel bridge structures. Copyright © 2004 John Wiley & Sons, Ltd. 相似文献
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This paper focuses on the efficiency of finite discrete element method (FDEM) algorithmic procedures in massive computers and analyzes the time-consuming part of contact detection and interaction computations in the numerical solution. A detailed operable GPU parallel procedure was designed for the element node force calculation, contact detection, and contact interaction with thread allocation and data access based on the CUDA computing. The emphasis is on the parallel optimization of time-consuming contact detection based on load balance and GPU architecture. A CUDA FDEM parallel program was developed with the overall speedup ratio over 53 times after the fracture from the efficiency and fidelity performance test of models of in situ stress, UCS, and BD simulations in Intel i7-7700K CPU and the NVIDIA TITAN Z GPU. The CUDA FDEM parallel computing improves the computational efficiency significantly compared with the CPU-based ones with the same reliability, providing conditions for achieving larger-scale simulations of fracture. 相似文献
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Classifying Human Activity Patterns from Smartphone Collected GPS data: A Fuzzy Classification and Aggregation Approach
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Smartphones have emerged as a promising type of equipment for monitoring human activities in environmental health studies. However, degraded location accuracy and inconsistency of smartphone‐measured GPS data have limited its effectiveness for classifying human activity patterns. This study proposes a fuzzy classification scheme for differentiating human activity patterns from smartphone‐collected GPS data. Specifically, a fuzzy logic reasoning was adopted to overcome the influence of location uncertainty by estimating the probability of different activity types for single GPS points. Based on that approach, a segment aggregation method was developed to infer activity patterns, while adjusting for uncertainties of point attributes. Validations of the proposed methods were carried out based on a convenient sample of three subjects with different types of smartphones. The results indicate desirable accuracy (e.g. up to 96% in activity identification) using of this method. Two examples are provided in the Appendix to illustrate how the proposed methods could be applied in environmental health studies. Researchers could tailor this scheme to fit a variety of research topics. 相似文献
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