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Xun Sun [5]Xunwei Sun [1]
  1.  15
    Geochemical Evidence for Oil and Gas Expulsion in Triassic Lacustrine Organic-Rich Mudstone, Ordos Basin, China.Tongwei Zhang, Xiangzeng Wang, Jianfeng Zhang, Xun Sun, Kitty L. Milliken, Stephen C. Ruppel & Daniel Enriquez - 2017 - Interpretation: SEG 5 (2):SF41-SF61.
    Forty-six core samples were collected from a deep well that penetrated organic-rich layers of the Chang 7, 8, and 9 members of the Yanchang Formation in the Ordos Basin. Tests for total organic content, Rock-Eval pyrolysis, X-ray diffraction mineralogy, and molecular composition of gases released from rock crushing were conducted. Analytical results indicate that TOC and clay contents are elevated. The organic matter -rich mudstone in the Triassic Yanchang Fm suggests good-to-excellent source potential for oil generation. Its thermal maturity is (...)
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  2.  23
    Controls on Pore Types and Pore-Size Distribution in the Upper Triassic Yanchang Formation, Ordos Basin, China: Implications for Pore-Evolution Models of Lacustrine Mudrocks.Lucy T. Ko, Robert G. Loucks, Kitty L. Milliken, Quansheng Liang, Tongwei Zhang, Xun Sun, Paul C. Hackley, Stephen C. Ruppel & Sheng Peng - 2017 - Interpretation: SEG 5 (2):SF127-SF148.
    Our main objectives are to learn if pore-evolution models developed from marine mudrocks can be directly applied to lacustrine mudrocks, investigate what controls the different pore types and sizes of Chang 7 organic matter -rich argillaceous mudstones of the Upper Triassic Yanchang Formation, and describe the texture, fabric, mineralogy, and thermal maturity variation in the Chang 7 mudstones. Lacustrine mudstones from nine cored wells along a depositional dip in the southeastern Ordos Basin, China, were investigated. Helium porosimetry, nitrogen adsorption, and (...)
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  3.  18
    Liquid Hydrocarbon Characterization of the Lacustrine Yanchang Formation, Ordos Basin, China: Organic-Matter Source Variation and Thermal Maturity.Xun Sun, Quansheng Liang, Chengfu Jiang, Daniel Enriquez, Tongwei Zhang & Paul Hackley - 2017 - Interpretation: SEG 5 (2):SF225-SF242.
    Source-rock samples from the Upper Triassic Yanchang Formation in the Ordos Basin of China were geochemically characterized to determine variations in depositional environments, organic-matter source, and thermal maturity. Total organic carbon content varies from 4 wt% to 10 wt% in the Chang 7, Chang 8, and Chang 9 members — the three OM-rich shale intervals. The Chang 7 has the highest TOC and hydrogen index values, and it is considered the best source rock in the formation. Geochemical evidence indicates that (...)
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  4.  5
    Training in Language Switching Facilitates Bilinguals’ Monitoring and Inhibitory Control.Cong Liu, Chin-Lung Yang, Lu Jiao, John W. Schwieter, Xun Sun & Ruiming Wang - 2019 - Frontiers in Psychology 10.
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  5. Research on Multistage Rotor Assembly Optimization Methods for Aeroengine Based on the Genetic Algorithm.Yue Chen, Jiwen Cui, Xun Sun & Shihai Cui - 2021 - Complexity 2021:1-14.
    The coaxiality and unbalance are the two important indexes to evaluate the assembly quality of an aeroengine. It often needs to be tested and disassembled repeatedly to meet the double-objective requirements at the same time. Therefore, an intelligent assembly method is urgently needed to directly predict the optimal assembly orientations of the rotors at each stage to meet the double-objective requirements simultaneously. In this study, an assembly optimization method for the multistage rotor of an aeroengine is proposed based on the (...)
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    A Dual Simple Recurrent Network Model for Chunking and Abstract Processes in Sequence Learning.Lituan Wang, Yangqin Feng, Qiufang Fu, Jianyong Wang, Xunwei Sun, Xiaolan Fu, Lei Zhang & Zhang Yi - 2021 - Frontiers in Psychology 12.
    Although many studies have provided evidence that abstract knowledge can be acquired in artificial grammar learning, it remains unclear how abstract knowledge can be attained in sequence learning. To address this issue, we proposed a dual simple recurrent network model that includes a surface SRN encoding and predicting the surface properties of stimuli and an abstract SRN encoding and predicting the abstract properties of stimuli. The results of Simulations 1 and 2 showed that the DSRN model can account for learning (...)
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