Automated Incident Detection (AID) is an important part of Advanced Trafﬁc Management and Information Systems (ATMISs). An automated incident detection system can effectively provide information on an incident, which can help initiate the required measure to reduce the inﬂuence of the incident. To accurately detect incidents in expressways, a Support Vector Machine (SVM) is used in this paper. Since the selection of optimal parameters for the SVM can improve prediction accuracy, the tabu search algorithm is employed to optimize the SVM parameters. The proposed model is evaluated with data for two freeways in China. The results show that the tabu search algorithm can effectively provide better parameter values for the SVM, and SVM models outperform Artiﬁcial Neural Networks (ANNs) in freeway incident detection.
Endoscopic submucosal dissection (ESD) has become the main treatment for early esophageal cancer. While treating the disease, ESD may also cause postoperative esophageal stricture, which is a global issue that needs resolution. Various methods have been applied to resolve the problem, such as mechanical dilatation, glucocorticoids, anti-scarring drugs, and regenerative medicine; however, no standard treatment regimen exists. This article describes and evaluates the strengths and limitations of new and promising potential strategies for the treatment and prevention of esophageal strictures.