Endoscopic grading of gastroesophageal flap valve (GEFV) is simple and reproducible and offers useful information for reflux activity. To investigate the potential correlation between GEFV grading and reflux finding score (RFS) in patients with laryngopharyngeal reflux disease (LPRD), 225 consecutive Patients with suspected LPRD who underwent both routine upper gastrointestinal endoscopy and laryngoscope were enrolled in our study. Patients with a RFS of more than 7 were diagnosed with LPRD. The GEFV was graded as I through IV according to Hill’s classification and was classified into two groups: normal GEFV group (grades I and II) and the abnormal GEFV group (grades III and IV). The percent of GEFV grades I to IV was 39.1%, 39.1%, 12.4%, and 9.3%, respectively. Age was significantly related to an abnormal GEFV (p = 0.002). Gender, BMI, smoke and alcohol were not related to GEFV grade. Fifty-one patients (22.67%) had positive RFS. Reflux finding scores were higher in GEFV grades III and IV than I and II (p < 0.05). Endoscopic grading of GEFV is well correlated with reflux finding score in patients with LPRD. This is a simple and useful technique that provides valuable diagnostic information of LPRD.

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Ship abnormal behavior detection is an essential part of maritime supervision. It can assist maritime departments to conduct real-time supervision on a certain sea area, avoid ship risks, and improve the efficiency of sea area supervision. Given the problems of complex detection methods, poor detection effectiveness, and low detection accuracy, a Gated Recurrent Unit (GRU) was proposed for ship abnormal behavior detection. Under the premise of introducing the attention mechanism into a GRU, the optimal GRU structure parameters were obtained through the intelligent algorithm to perform deeper feature extraction and train the ship abnormal behavior based on the optimized GRU neural network, so as to realize the detection and recognition of the trajectory data to be measured. Finally, based on the public data set and the trajectory data of the inward and outward ports of ships issued by Nanjing Section, Jiangsu Maritime Bureau, the TensorFlow frame was used to establish an abnormal behavior detection model. The simulation results demonstrated that the abnormal behavior detection model shortened the abnormal detection time. The abnormal behavior detection model used in the detection of ship abnormal behavior enhanced the accuracy and stability of the abnormal behavior identification and verified the validity and superiority of this method.