Predicting the trajectories of neighboring vehicles is essential to evade or mitigate collision with traffic participants. However, due to inadequate previous information and the uncertainty in future driving maneuvers, trajectory prediction is a difficult task. Recently, trajectory prediction models using deep learning have been addressed to solve this problem. In this study, a method of early warning is presented using fuzzy comprehensive evaluation technique, which evaluates the danger degree of the target by comprehensively analyzing the target’s position, horizontal and vertical distance, speed of the vehicle, and the time of the collision. Because of the high false alarm rate in the early warning systems, an early warning activation area is established in the system, and the target state judgment module is triggered only when the target enters the activation area. This strategy improves the accuracy of early warning, reduces the false alarm rate, and also speeds up the operation of the early warning system. The proposed system can issue early warning prompt information to the driver in time and avoid collision accidents with accuracy up to 96%. The experimental results show that the proposed trajectory prediction method can significantly improve the vehicle network collision detection and early warning system.

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The complete mitochondrial genome of the fish leech Zeylanicobdella arugamensis from China has been determined for the first time in this study. It was 16,161 bp in length and consisted of 22 tRNA genes, two rRNA genes, 13 protein-coding genes (PCGs), and one control region. The nucleotide base content of Z. arugamensis mitogenome was 35.5% A, 10.4% C, 10.4% G, and 43.7% T. Start codon ATG was used in PCGs, while most of the termination codons are incomplete T or TA. The tRNA genes were ranged from 59 bp (tRNA-Arg and tRNA-Glu) to 69 bp (tRNA-Gln and tRNA-Cys) in length. The phylogenetic tree was constructed and suggested that Z. arugamensis has closer relationship to the Poecilobdella manillensis, Erpobdella octoculata, Hirudo hipponia, Whitmania acranulata, Whitmania pigra, Whitmania Laevis, Hirudo verbaba, and Hirudo medicinalis, and that they constitute a sister group.

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In order to improve the quality of information-based teaching in colleges and universities, promote the penetration of information technology into the classroom, and improve the teaching effect, based on the TPACK framework, this paper designs the information-based teaching cloud space in colleges and universities, and constructs the system framework by combining the learning theory and teaching needs with the characteristics of the cloud space. Teaching cloud space is to carry out research from four dimensions: technology dimension, teaching dimension, discipline dimension and deep learning dimension, to realize the integration of technology and education and teaching ideas, and to build a personalized teaching cloud space. The research results show that the information-based teaching cloud space based on TPACK theory can improve the teaching of practical courses and stimulate students’ curiosity and enthusiasm for learning.

Abstract-With the recent development in ICT environment, College English learning and teaching mode has undergone a huge transformation. The concept of flipped classroom and its features were outlined. The problems in current College English teaching and how to construct a flipped classroom teaching model were elaborated. The role a flipped mode plays in promoting College English teaching and problems in its implementation were pointed out. This paper is intended to serve as a guide to instructors seeking to develop, implement, and evaluate innovative and practical strategies to transform students' learning experience.