Vehicle-to-everything (V2X) technology will significantly enhance the information perception ability of drivers and assist them in optimizing car-following behavior. Utilizing V2X technology, drivers could obtain motion state information of the front vehicle, non-neighboring front vehicle, and front vehicles in the adjacent lanes (these vehicles are collectively referred to as generalized preceding vehicles in this research). However, understanding of the impact exerted by the above information on car-following behavior and traffic flow is limited. In this paper, a car-following model considering the average velocity of generalized preceding vehicles (GPV) is proposed to explore the impact and then calibrated with the next generation simulation (NGSIM) data utilizing the genetic algorithm. The neutral stability condition of the model is derived via linear stability analysis. Numerical simulation on the starting, braking and disturbance propagation process is implemented to further study features of the established model and traffic flow stability. Research results suggest that the fitting accuracy of the GPV model is 40.497% higher than the full velocity difference (FVD) model. Good agreement between the theoretical analysis and the numerical simulation reveals that motion state information of GPV can stabilize traffic flow of following vehicles and thus alleviate traffic congestion.
It is of great practical and theoretical significance to identify driver fatigue state in real time and accurately and provide active safety warning in time. In this paper, a non-invasive and low-cost method of fatigue driving state identification based on genetic algorithm optimization of generalized regression neural network model is proposed. The specific work is as follows: (1) design simulated driving experiment and real driving experiment, determine the fatigue state of drivers according to the binary Karolinska Sleepiness Scale (KSS), and establish the fatigue driving sample database. (2) Improved Multi-Task Cascaded Convolutional Networks (MTCNN) and applied to face detection. Dlib library was used to extract the coordinate values of face feature points, collect the characteristic parameters of driver's eyes and mouth, and calculate the Euler Angle parameters of head posture. A fatigue identification model was constructed by using multiple characteristic parameters. (3) Genetic Algorithm (GA) was used to find the optimal smooth factor of Generalized Regression Neural Network (GRNN) and construct GA-GRNN fatigue driving identification model. Compared with K-Nearest Neighbor (KNN), Random Forest (RF), and GRNN fatigue driving identification algorithms. GA-GRNN has the best generalization ability and high stability, with an accuracy of 93.3%. This study provides theoretical and technical support for the application of driver fatigue identification.
Fruit bagging is a very effective method for study of fruit qualities and anthocyanin synthesis. The characterization of differentially expressed proteins that were isolated from both bagged and normal fruit skin tissue is apparently an essential parameter for understanding the effect of shading on fruit qualities and to understand the mechanism of fruit coloring in Pyrus communis. Proteome maps of both bagged and normal P. communis 'Placer' fruit skin were obtained by performing two-dimensional electrophoresis analysis and compared to assess the extent to which protein distribution differed in pear skin. The comparative analysis showed 38 differentially expressed proteins between the two samples: with three protein spots up-regulated and 35 down-regulated in the bagged fruit. Differentially expressed protein spots were subjected to matrix-assisted laser desorption ionization time of flight (MALDI-TOF) analysis and the data compared to that of known proteins to deduce their possible functions. Of these, 21 protein spots were identified and classified into functional classes. These identified proteins were mainly involved in photosynthesis, signal transduction, energy pathway, protein folding and assembly, and carbohydrate and acidity metabolisms, and were under-expressed in bagged fruit skins. This work provides a first characterization of the proteome changes in response to fruit bagging treatment in red pears.
Effects of milrinone on serum interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), cystatin C (Cys-C) and cardiac functions of patients with chronic heart failure were analyzed to investigate the value of milrinone in chronic heart failure. A total of 70 patients diagnosed with chronic heart failure were selected and randomly divided into treatment group (n=35) and control group (n=35). All patients were treated with conventional anti-heart failure therapy, and patients in the treatment group received milrinone on the basis of conventional therapy. The general data of patients, such as age, sex and course of chronic heart failure, were collected; the levels of serum IL-6, TNF-α and Cys-C before and after treatment were compared between the groups, and the cardiac function indexes were also compared, including cardiac output (CO), stroke volume (SV), left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVDd), left ventricular end-systolic diameter (LVSd) and brain natriuretic peptide (BNP) level. Besides, the curative effects and adverse reactions in the two groups were recorded. The results revealed that serum IL-6, TNF-α and Cys-C levels had no significant difference between the two groups before treatment; however, the curative effect in the treatment group was significantly superior to that in control group (p<0.05); after treatment, CO, SV and LVEF in both groups were obviously increased, but LVDd, LVSd and BNP levels were obviously decreased; the curative effect in the treatment group was significantly superior to that in control group (p<0.05); heart rate in both groups was obviously decreased after treatment (p<0.05); the total effective rate in the treatment group was significantly higher than that in control group after treatment (p<0.05). In conclusion, based on the conventional anti-heart failure therapy, the application of milrinone can reduce the serum IL-6, TNF-α and Cys-C levels and improve the cardiac functions of patients effectively.
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