Objective: Robot-assisted neuro-rehabilitation therapy plays a central role in upper extremity recovery of stroke. Even though, the efficacy of robotic training on upper extremity is not yet well defined and scant attention has been devoted to its potential effect on lower extremity. In this paper, the aim was to compare efficacy on upper and lower extremities between robot-assisted training (RAT) and therapist-mediated enhanced upper extremity therapy (EUET).Methods: A randomized clinical trial involving 172 stroke survivors was conducted in China. All participants received either RAT or EUET for 3 weeks. The Fugl-Meyer assessment upper extremity subscale (FMA-UE), Fugl-Meyer assessment lower extremity subscale (FMA-LE), and Modified Barthel Index (MBI) were administered at baseline, mid-treatment (one week after treatment start), and posttreatment. Results: Participants in RAT group showed a significant improvement in hemiplegia extremity, which was non-inferior to EUET group in FMA-UE (p<0.05), while suggesting greater motor recovery of lower extremity in FMA-LE (p<0.05) compared with EUET group. A marked increase in MBI was observed within groups, however, no significant difference was detected between groups.Conclusion: RAT is non-inferior in reducing impairment of upper extremity and appears to be superior in that of lower extremity compared with EUET for stroke survivors.
A novel failure analysis method named D‐vine copula Bayesian Network is proposed, aimed to extract fault correlation information from various condition monitoring channels of floating offshore wind turbine components, quantify risk probability and consequence, and obtain risk priority of components considering condition correlation. First, a copula Bayesian model is established based on the Bayesian network and D‐vine copula theory. Then, a risk consequence calculation method considering relative loss is developed. Finally, critical failure items are identified by a risk matrix. The proposed technique is expected to: (i) Release the limitation that parent nodes with condition monitoring input are processed as independent in Bayesian analysis. (ii) Provide an alternative way for presenting relationships between nodes instead of conditional probability tables. (iii) Simplify the calculation of high‐dimensional copula. This study screened out high‐risk level components of floating offshore wind turbines, and operation recommendations avoiding potential failure risk are put forward. The comparative results demonstrate the feasibility and reliability of the proposed method.
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