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  • Frontiers Media SA  (2)
  • Bao, Guanjun  (2)
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  • Frontiers Media SA  (2)
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  • 1
    Online Resource
    Online Resource
    Frontiers Media SA ; 2022
    In:  Frontiers in Psychology Vol. 13 ( 2022-7-28)
    In: Frontiers in Psychology, Frontiers Media SA, Vol. 13 ( 2022-7-28)
    Abstract: The evaluation of the surgical readiness of patients plays an important role in clinical care. Preoperative readiness assessment is needed to identify the inadequacy among surgical patients, which provides guide for interventions to improve patients’ preoperative readiness. However, there is a paucity of high-level, quality tool that evaluate surgical readiness of patients in China. The purpose of this study is to translate the Preoperative Assessment of Readiness Tool (PART) into Chinese and determine the reliability and validity of the Chinese version in the population of surgical patients. Methods Using a standard translation-backward method, the original English version of PART was translated into Chinese. A convenient sampling of 210 surgical patients was recruited from 6 hospitals in Zhejiang Province to test the psychometric properties of this scale including internal consistency, split-half reliability, content validity, structure validity, and floor/ceiling effect. Results A total of 194 patients (92%) completed questionnaires. The Chinese version of PART achieved Cronbach’s alphas 0.948 and McDonald’s omega coefficient 0.947, respectively, for the full scale. The estimated odd-even split-half reliability was 0.959. The scale-level content validity index was 0.867, and the items content validity index ranged from 0.83 to 1.0.The output of confirmatory factor analysis (CFA) revealed a two-factor model (χ 2 = 510.96; df = 86; p & lt; 0.001; root mean square error approximation = 0.08) with no floor/ceiling effect. Conclusion The Chinese version of PART demonstrated acceptable reliability and validity among surgical patients. It can be used to evaluate patients’ preoperative preparation and help health professionals provide proper preoperative support.
    Type of Medium: Online Resource
    ISSN: 1664-1078
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2563826-9
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  • 2
    Online Resource
    Online Resource
    Frontiers Media SA ; 2022
    In:  Frontiers in Neurorobotics Vol. 16 ( 2022-5-13)
    In: Frontiers in Neurorobotics, Frontiers Media SA, Vol. 16 ( 2022-5-13)
    Abstract: Rock drilling robots are able to greatly reduce labor intensity and improve efficiency and quality in tunnel construction. However, due to the characteristics of the heavy load, large span, and multi-joints of the robot manipulator, the errors are diverse and non-linear, which pose challenges to the intelligent and high-precision control of the robot manipulator. In order to enhance the control accuracy, a hybrid positional error compensation method based on Radial Basis Function Network (RBFN) and Light Gradient Boosting Decision Tree (LightGBM) is proposed for the rock drilling robot. Firstly, the kinematics model of the robotic manipulator is established by applying MDH. Then a parallel difference algorithm is designed to modify the kinematics parameters to compensate for the geometric error. Afterward, non-geometric errors are analyzed and compensated by applying RBFN and lightGBM including features and kinematics model. Finally, the experiments of the error compensation by combing combining the geometric and non-geometric errors verify the performance of the proposed method.
    Type of Medium: Online Resource
    ISSN: 1662-5218
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2453002-5
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