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    Selective hydrogenation catalyst system and process for preparing the same and its use
    32.
    发明授权
    Selective hydrogenation catalyst system and process for preparing the same and its use 有权
    选择性氢化催化剂体系及其制备方法及其应用

    公开(公告)号：US06459008B1

    公开(公告)日：2002-10-01

    申请号：US09599612

    申请日：2000-06-23

    Applicant: Wei Dai ,  Jing Zhu ,  Helong Li ,  Yanlai Guo ,  Wei Mu ,  Hui Peng ,  Xin Chen

    Inventor： Wei Dai ,  Jing Zhu ,  Helong Li ,  Yanlai Guo ,  Wei Mu ,  Hui Peng ,  Xin Chen

    IPC: C07C402

    CPC classification number: C10G45/40 ,  B01J23/6447

    Abstract: A selective hydrogenation catalyst system, and a process for its preparation and its use. The catalyst system of the invention comprises a support material, a Pd-containing catalyst component and a Bi-containing cocatalyst component. The catalyst system of the invention is manufactured by impregnating the support material simultaneously or separately with Pd-containing solution, Bi-containing solution or/and one or more other cocatalyst solutions, and then drying and calcining. The activity and selectivity of the catalyst system of the invention, in selective hydrogenation of acetylenic and diolefmic compounds in hydrocarbon feeds, are significantly improved, while the green oil formation and carbon deposit on the catalyst reduced, and the service life increased and production costs decreased.

    Abstract translation: 选择性氢化催化剂体系及其制备方法及其应用。 本发明的催化剂体系包括载体材料，含Pd催化剂组分和含Bi助催化剂组分。 本发明的催化剂体系是通过与含Pd溶液，含Bi的溶液或一种或多种其它助催化剂溶液同时或分开地浸渍载体材料，然后干燥和煅烧来制造的。 本发明的催化剂体系在烃类进料中炔属和二烯化合物的选择性氢化中的活性和选择性得到显着提高，而催化剂上的绿色油形成和碳沉积减少，使用寿命增加，生产成本降低 。

    MODEL BASED ON MACHINE LEARNING-RADIOMICS AND APPLICATION THEREOF
    34.
    发明申请
    MODEL BASED ON MACHINE LEARNING-RADIOMICS AND APPLICATION THEREOF 有权

    公开(公告)号：US20220076826A1

    公开(公告)日：2022-03-10

    申请号：US17013661

    申请日：2020-09-07

    Applicant: Fanxin Zeng ,  Fanwei Zeng ,  Jing Zhu ,  Qianrong Xie ,  Pingxi Wang ,  Yue Chen

    Inventor： Fanxin Zeng ,  Fanwei Zeng ,  Jing Zhu ,  Qianrong Xie ,  Pingxi Wang ,  Yue Chen

    IPC: G16H50/20 ,  G16H50/30

    Abstract: The invention discloses a model based on machine learning-radiomics which is a Nomogram model; the establishment of said Nomogram model comprising following steps: S1. collecting data and predicting factor selection; S2. combining selected 6 radiomics characteristics according to contribution weighting, developing a rad-score model; S3. establishing different diagnosis models, comparing performances of the diagnosis models in the diagnosis of osteoporosis and osteopenia, wherein said diagnosis models comprise: Clinics model, Radiomics model, and Combined model; S4. performing visualization processing on combined model by using the “rms” packet in R software to obtain Nomogram model; S5. validation of the Nomogram model. The invention combines characteristics of the radiomics and clinical risk factors to establish a combined Nomogram model to diagnose conditions of osteoporosis and osteopenia of patients. The diagnosis method can accurately distinct patients with osteoporosis from patients with osteopenia, having great application value for selection of clinical therapeutic regimen.

    Pet snuffle mat
    35.
    外观设计
    Pet snuffle mat 有权

    公开(公告)号：USD935702S1

    公开(公告)日：2021-11-09

    申请号：US29745110

    申请日：2020-08-04

    Applicant: Jing Zhu

    Designer： Jing Zhu

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