ABSTRACT
Control forgetting accounts for most of the current unsafe incidents. In the research field of radar surveillance control, how to avoid control forgetting to ensure the safety of flights is becoming a hot issue which attracts more and more attention. Meanwhile, aviation safety is substantially influenced by the way of eye movement. The exact relation of control forgetting with eye movement, however, still remains puzzling. Motivated by this, a control forgetting prediction method is proposed based on the combination of Convolutional Neural Networks and Long-Short Term Memory (CNN-LSTM). In this model, the eye movement characteristics are classified in terms of whether they are time-related, and then regulatory forgetting can be predicted by virtue of CNN-LSTM. The effectiveness of the method is verified by carrying out simulation experiments of eye movement during flight control. Results show that the prediction accuracy of this method is up to 79.2%, which is substantially higher than that of Binary Logistic Regression, CNN and LSTM (71.3%, 74.6%, and 75.1% respectively). This work tries to explore an innovative way to associate control forgetting with eye movement, so as to guarantee the safety of civil aviation.
Subject(s)
Aviation , Eye Movements , Neural Networks, Computer , Computer Simulation , Memory, Long-TermABSTRACT
BACKGROUND: Neck pain is a common discomfort or more intense forms of pain in the cervical region. Neck pain has a large impact on individuals and their families, communities, healthcare systems, and businesses throughout the world. Therapeutic strategies are widely used for patients with neck pain in clinical practice, but the effectiveness of each therapeutic strategy is still unclear. The aim of this study is to assess the efficacy and safety of therapeutic strategies for neck pain. METHOD: Seven electronic databases will be searched regardless of publication date or language. Randomized controlled trials will be included if they recruited participants with neck pain for assessing the effect of each therapy. Primary outcomes will include pain score. The risk of bias will be assessed by 2 authors using the Cochrane tool of risk of bias. Network meta-analysis in random effects model will be conducted to estimate the indirect and mixed effects of therapeutic strategies for neck pain by R-3.5.1 software. The confidence in cumulative evidence will be assessed by grading of recommendations assessment, development, and evaluation. RESULTS: This study will be to assess the effect and safety of therapeutic strategies for neck pain. CONCLUSIONS: This study will assess the effect of different therapeutic strategies for neck pain and provide reliable evidence for the choice of treatments. SYSTEMATIC REVIEW REGISTRATION: PROSPERO (CRD42019102385).
