Electric vehicles generally have a better noise, vibration, and harshness quality than traditional vehicles due to the relatively quiet electric motors. By contrast, the noise, vibration, and harshness issues of the driveline system become more outstanding and significant in the absence of the “masking effect” by the engine. The electrification of the powertrain has also brought many changes in the sources or transmission of vibration, which has led to some new noise, vibration, and harshness issues. Specifically, the intense vibration of the prototype bus appears when driving in third gear, which makes the passengers uncomfortable. This paper presents an efficient analytical strategy for identifying the resonance sources and vibration transmission for a pure electric bus. The strategy incorporates order analysis, operating deflection shape, and transfer path analysis. Order analysis shows that the resonance is primarily caused by the second-order excitation associated with the driveline, and the vibration sources are further identified using operating deflection shape analysis. Moreover, the vibration transfer paths from the driveline to the bus floor are quantitatively determined by the transfer path analysis method. The results show that the coupling vibration of the powertrain and the rear drive axle, which amplifies the resonance of the whole driveline, is transmitted to the bus floor primarily through powertrain mounts and V rods. Based on the results, the design and structure modifications of the driveline and transfer paths are recommended to handle this issue. The proposed identification strategy would be beneficial for accurate and efficient engineering troubleshooting on the vibration issues.

Small GTPases play critical roles in the regulation of plant growth and development. However, the mechanism of small GTPases in plant response to virus infection remains largely unknown. Here, a Rho-type GTPase NtRHO1 was identified as one of up-regulated genes after tobacco mosaic virus (TMV) infection. Subcellular localization of NtRHO1 showed that it was localized in the cytoplasm, plasma membrane as well as nucleus. Transient overexpression of NtRHO1 in Nicotiana benthamiana plants accelerated virus reproduction and led to more reactive oxygen species production. By contrast, silencing of NtRHO1 reduced the sensitivity of N. benthamiana plants to TMV-GFP. Further explorations showed that there existed a direct interaction between NtRHO1 and NtWRKY50, a positive regulator of N. benthamiana plants response to virus infection. Yeast one-hybrid and electrophoretic mobility shift assays showed that this regulation was related to NtWRKY50’s binding capacity to the WK-box of PR1 promoter, which was weakened by the interaction between NtRHO1 and NtWRKY50. Thus, the role of a novel small GTPase NtRHO1 in the plant-pathogen interaction was explored and its mechanism was proposed.

This paper presents a vibration-based vehicle classification system using distributed optical vibration sensing (DOVS) technology and describes a comprehensive classification method including signal processing and feature extraction. With low maintenance costs, this system can collect vehicle classification data in a larger scale. At first, it utilizes an embedded sensing fiber as a distributed sensor to collect traffic-induced vibration signals, and then extracts several features from the raw signals to estimate axle configurations and identify vehicle categories. At the same time, an empirical mode decomposition (EMD)-based method is applied to reconstruct signals for features extraction, and then several extraction algorithms are proposed to obtain the axle configuration, moving speed, and frequency-domain feature of each vehicle. When all features are extracted, a multi-step classifier is designed to categorize vehicles into different classes. In addition, to evaluate the classification performance of this system, a prototype system was installed on a relief road in Shanghai, China using precast concrete pavement technology. With an overall accuracy of 89%, the test results show a good performance of this classification system.

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Effective traffic management requires use of monitoring technologies to extract traffic parameters that describe the characteristics of vehicles. This article presents a vibration-based traffic monitoring system to detect traffic parameters comprehensively, including the three major desired characteristics of vehicles: position, speed, and category. Using distributed optical technology, this system can monitor traffic parameters on a large scale. Firstly, distributed optical fiber was embedded into pavement as a series of vibration sensors to collect pavement vibration information. Then, a signal processing method, based on wavelet transform and short-time energy, was applied to reconstruct vibration signals for vibration features extraction. Estimation methods of traffic parameters were proposed based on the vibration characteristics in time, space and frequency domain. To evaluate the performance of this traffic monitoring system, a prototype system was installed on a highway in Shanghai, China. This system shows good accuracy in position locating (± 14.639 cm), category classification (86.98 %), and speed estimation (± 3.046 km/h). Meanwhile, results indicate this system provides a better monitoring performance at high speed.