Thriving at work: A meta‐analysis

Thriving at work refers to a positive psychological state characterized by a joint senseof vitality and learning. On the basis of Spreitzer and colleagues' model, we present acomprehensive meta‐analysis of antecedents and outcomes of thriving at work(K= 73 independent samples,N= 21,739 employees). Results showed that thrivingat work is associated with individual characteristics, such as psychological capital(rc= .47), proactive personality (rc= .58), positive affect (rc= .52), and work engage-ment (rc= .64). Positive associations were also found between thriving at work andrelational characteristics, including supportive coworker behavior (rc= .42), support-ive leadership behavior (rc= .44), and perceived organizational support (rc= .63).Moreover, thriving at work is related to important employee outcomes, includinghealth‐related outcomes such as burnout (rc=−.53), attitudinal outcomes such ascommitment (rc= .65), and performance‐related outcomes such as task performance(rc= .35). The results of relative weights analyses suggest that thriving exhibits small,albeit incremental predictive validity above and beyond positive affect and workengagement, for task performance, job satisfaction, subjective health, and burnout.Overall, the findings of this meta‐analysis support Spreitzer and colleagues' modeland underscore the importance of thriving in the work conte

Defect size estimation method for magnetic flux leakage signals using convolutional neural networks

A method for defect size estimation of magnetic flux leakage (MFL) signals using convolutional neural networks (CNNs) is proposed to overcome the problem of quantitative identification of pipeline MFL testing. The model mainly includes two modules: defect classification and defect size regression. The former is used to realise data fusion, feature extraction and tasks of three components (axial, circumferential and radial) of the MFL defect signal. The defect size regression module includes seven CNNs, which realise the size estimation of different types of defect. The input is a different type of defect in the defect classification module and the output is the length, width and depth information of the defect. Finally, the training and prediction are conducted using a defect dataset. The results show that the proposed method can effectively identify the MFL defect size of the pipeline.</jats:p

Safety and efficacy of low-intensity versus standard monitoring following intravenous thrombolytic treatment in patients with acute ischaemic stroke (OPTIMISTmain): an international, pragmatic, stepped-wedge, cluster-randomised, controlled non-inferiority trial

Background: The universally accepted best practice protocol for monitoring patients who receive intravenous thrombolysis for acute ischaemic stroke was established in the 1990s. However, the protocol is burdensome for nurses, disrupts the sleep of patients, and is potentially less relevant in patients at low risk of symptomatic intracerebral haemorrhage. We aimed to assess whether implementing a low-intensity monitoring protocol would be as safe and effective as standard high-intensity monitoring for patients with acute ischaemic stroke at low risk. Methods: OPTIMISTmain was an international, pragmatic, multicentre, stepped-wedge, cluster-randomised, controlled, non-inferiority, blinded-endpoint trial conducted at hospitals (clusters) in eight countries. It was designed to test the non-inferiority of a low-intensity monitoring protocol to a standard protocol among consecutive adults with acute ischaemic stroke who were clinically stable with mild to moderate neurological impairment (score </p