An accurate, rapid and effective method was established for determination of eugenol in plasma, muscle, skin, liver, kidney and gill of fish using gas chromatography-ion trap tandem mass spectrometry. Samples of muscle, skin, liver, kidney and gill were prepared using the modified QuEChERS (quick, easy, cheap, effective, rugged and safe) procedure, and a plasma sample was prepared by a liquid-liquid extraction procedure. Eugenol was monitored in <7 min using an electron-ionization source in MS/MS mode and quantified by an internal standard of eugenol-d . The limit of detection was 5.0 μg/kg, and the limit of quantification was 10.0 μg/kg. The calibration curve was linear in the range of 5-1000 μg/L (R = 0.9996). Intra- and inter-day precisions of eugenol expressed as relative standard deviation were within 9.74%, and the accuracy exhibited a relative error ranging from -2.20 to 8.89%. The developed method was successfully used to study the elimination regularity of eugenol in mandarin fish.

The electric truck frame as a vital load-bearing component has aroused growing attentions due to its enormous potential in lightweight. However, few systematic studies have been performed on the multi-objective topological design of the frame attributable to its complexity on loading and conflicting objectives. This paper aims to develop a multi-objective topology optimization strategy of the electric truck frame based on the hybrid decision making method combining orthogonal test design (OTD) and analytic hierarchy process (AHP). The hybrid strategy is performed to obtain a new set of weight ratio combination from objective data and subjective judgment. The topological results show that the overall performance of the optimal frame is better than any of the methods applied alone. By comparing, it is found that the strength and stiffness of the optimal frame is higher than that of the original frame from the perspective of static conditions, and the low-order natural frequency of the optimal frame is significantly improved. It demonstrates that the proposed approach could be as an effective tool for multi-objective topology optimization of the electric truck frame in seeking lightweight and comprehensive mechanical performance. The hybrid strategy might be expected to provide some guidance for more complicated engineering problems. INDEX TERMS Electric truck frame, multi-objective design, topology optimization, orthogonal test, analytic hierarchy process.

Long tunnels often collapse during the construction period. To ensure personnel safety, the geological characteristics must be predicted before tunnel face excavation. In this study, the ground-penetrating radar (GPR) technique is introduced to obtain information regarding the tunnel excavation face at a certain interval. The amplitude of the radar echo signal is expressed as a function of the position and travel time. A B-scan strategy is selected for the GPR to obtain tunnel information. A frequency-domain ( w -k) focusing algorithm, namely, a synthetic aperture radar focusing algorithm, is applied to focus scattered radar signals to obtain focused images. A low-pass filter is designed to remove noises from the original signals. The contours of target objects are extracted from the background information using the edge detection technique. Space coordinate values of the objects are converted to polar coordinates using the Hough transform algorithm for 3D modeling. Visual C++ and AutoCAD are combined to develop a 3D CAD model to help managers in controlling the construction process. The system creates 3D visualization model images and evaluates the geological characteristics of the tunnel excavation faces. The Taigu Tunnel located in the Shanxi Province of China is taken as a case study. A procedure for the geological analysis of this tunnel is introduced in detail, and a 3D image model is built. The results show that the 3D model can help predict rock compositions and locate potential hazards. Moreover, it has better accuracy than conventional models and can be applied to similar transportation construction projects.

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This paper addresses the problem of evaluating vehicle failure modes efficiently during the driving process. Generally, the most critical factors for preventing risk in potential failure modes are identified by the experience of experts through the widely used failure mode and effect analysis (FMEA). However, it has previously been difficult to evaluate the vehicle failure mode with crisp values. In this paper, we propose a novel hybrid scheme based on a cost-based FMEA, fuzzy analytic hierarchy process (FAHP), and extended fuzzy multi-objective optimization by ratio analysis plus full multiplicative form (EFMULTIMOORA) to evaluate vehicle failure modes efficiently. Specifically, vehicle failure modes are first screened out by cost-based FMEA according to maintenance information, and then the weights of the three criteria of maintenance time (T), maintenance cost (C), and maintenance benefit (B) are calculated using FAHP and the rankings of failure modes are determined by EFMULTIMOORA. Different from existing schemes, the EFMULTIMOORA in our proposed hybrid scheme calculates the ranking of vehicle failure modes based on three new risk factors (T, C, and B) through fuzzy linguistic terms for order preference. Furthermore, the applicability of the proposed hybrid scheme is presented by conducting a case study involving vehicle failure modes of one common vehicle type (Hyundai), and a sensitivity analysis and comparisons are conducted to validate the effectiveness of the obtained results. In summary, our numerical analyses indicate that the proposed method can effectively help enterprises and researchers in the risk evaluation and the identification of critical vehicle failure modes.