Prediction of Large Scale Spatio-temporal Traffic Flow Data with New Graph Convolution Model

Prompt and accurate prediction of traffic flow is quite useful. It will help traffic administrator to analyze the road occupancy status and formulate dynamic and flexible traffic control in advance to improve the road capacity. It can also provide more precise navigation guidance for the road users in future. However, it is hard to predict spatiotemporal traffic flow data in large scale promptly with high accuracy caused by complex interrelation and nonlinear dynamic nature. With development of deep learning and other technologies, many prediction networks could predict traffic flow with accumulated historical data in time series. In consideration of the regional characteristics of traffic flow, the emerging Graph Convolutional Network (GCN) model is systematically introduced with representative applications. Those successful applications provide a possible way to contribute fast and proper traffic control strategies that could relieve traffic pressure, reduce potential conflict, fasten emergency response, etc

An alternative reliability method to evaluate the regional traffic congestion from GPS data obtained from floating cars

Abstract Fast and reliable evaluation of regional traffic congestion is beneficial to more effective traffic control. Based on data accumulation in modern society, more and more data‐driven methods are proposed. However, it is still not easy to process the raw data to an interpretable level in practical applications. In this article, the GPS data are obtained from floating cars covering a large scale region in Xi'an, China. To link the original data to the spatiotemporal relationship of driving behaviour, a pre‐processing method with specified time–frequency rules is proposed. Through map matching and landmark mapping, it can be seen that the data dispersion degree has decreased and the quality of the original data has been improved. At the same time, deep learning methods and non‐parametric survival analysis methods are used to compare and evaluate traffic congestion. In addition, four different distributions (Exponential, Weibull, Log‐normal, and Log‐logistic) are tested to fit the accelerated failure time model (AFT), which is then compared with the Cox proportional hazards model (Cox). It is concluded that the most suitable parameter model for the test section of Xi'an South Second Ring Road is AFT (Lognormal). All those methods are tested on a randomly selected segment on the ring road in Xi'an. The results suggest dramatic improvement of data quality and successful evaluation of traffic conditions with high reliability. Potential application could be effective methods for traffic control and management in the smart city

Cell Membrane-Interrupting Antimicrobial Peptides from Isatis indigotica Fortune Isolated by a Bacillus subtilis Expression System

The situation of drug resistance has become more complicated due to the scarcity of plant resistance genes, and overcoming this challenge is imperative. Isatis indigotica has been used for the treatment of wounds, viral infections, and inflammation for centuries. Antimicrobial peptides (AMPs) are found in all classes of life ranging from prokaryotes to eukaryotes. To identify AMPs, I. indigotica was explored using a novel, sensitive, and high-throughput Bacillus subtilis screening system. We found that IiR515 and IiR915 exhibited significant antimicrobial activities against a variety of bacterial (Xanthomonas oryzae, Ralstonia solanacearum, Clavibacter michiganensis, and C. fangii) and fungal (Phytophthora capsici and Botrytis cinerea) pathogens. Scanning electron microscope and cytometric analysis revealed the possible mechanism of these peptides, which was to target and disrupt the bacterial cell membrane. This model was also supported by membrane fluidity and electrical potential analyses. Hemolytic activity assays revealed that these peptides may act as a potential source for clinical medicine development. In conclusion, the plant-derived novel AMPs IiR515 and IiR915 are effective biocontrol agents and can be used as raw materials in the drug discovery field