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A classifying method on bulk blue ballpoint pen ink has been studied by Fourier transform infrared (FTIR) spectroscopy. By using this method, a total of 108 blue ink samples have been divided into two groups depending on their main component. Spectral characteristics of these inks such as frequency and absorbance are described by way of artificial intelligence of pattern recognition, and 35 subgroups from the 108 inks are distinguished by their correlation coefficient (λ). Under heat or exposure to ultraviolet light a mode of change in the age of the inks has been obtained. This approach has provided a reliable and nondestructive method for the characterization of bulk ballpoint writing ink, and more importantly, it might be a basis for dating of the blue ballpoint pen ink.

Abstract:The combined effect of the use of carbon fiber and seawater and the molecular structure on the tribological behavior of various polymer materials under natural seawater lubrication was investigated. After the investigation, the wear morphology of the contact surface was observed by a laser scanning confocal microscope, and the texture of the wear scars and tracks were presented in 3D profiles. Moreover, the mechanism of mixed lubrication and wear resistance was analyzed. The results demonstrated that the friction coefficient of carbon fiber-reinforced polyetheretherketone (CFRPEEK) is the lowest and fluctuates at approximately 0.11. Moreover, the seven polymer materials in ascending order of friction coefficients are CFRPEEK, carbon fiber-reinforced polyamide-imide, polytetrafluoroethylene, polyoxymethylene, polyetheretherketone (PEEK), acrylonitrile butadiene styrene resin, and glass fiber-epoxy resin. More critically, the simultaneous incorporation of deposition, polymeric scrap, hydrophilic groups, and seawater resulted in a decrease in the friction and wear of polymer materials under seawater lubrication. This observation implies that a synergistic friction-reducing and wear-resistant effect exists between carbon fiber, seawater, and the molecular structure of PEEK. As a result, a highly effective polymer material was discovered, CFRPEEK, which has the lowest friction coefficient of 0.11 and lowest wear rate of 2 × 10 -5 mm 3 ·(N·m) −1 among the polymer materials; this validates the selection of dual friction pairs for seawater hydraulic components.

This paper presents a regionalized vulnerability curve-building approach to vulnerability and risk assessment of wheat subjected to drought that uses the Environmental Policy Integrated Climate (EPIC) model and statistical analysis. We defined wheat vulnerability as the degree to which a wheat production system is likely to experience yield loss due to a perturbation or drought hazard. Wheat vulnerability in a given region is thus the yield loss divided by the drought hazard index (DHI yield losses and associated DHIs, wheat drought vulnerability curves can be developed. We propose that agricultural systems be considered uniform within each wheat-planting region and different in different regions, according to territorial differentiation, when regionalized vulnerability curves are built. Based on this principle, a detailed regional crop calendar was improved, and optimized wheat varieties were refined that can differentiate agricultural systems within wheat-planting regions. The crop calendar was improved based on the assumption that local farmers have perfect knowledge in selecting sowing and harvesting dates. The wheat varieties were optimized by adjusting the genetic parameters of wheat in the EPIC model using the Shuffled Complex Evolution algorithm-University of Arizona (SCE-UA) method. Based on these improvements and innovations, the precision of most vulnerability curves was improved, and the curves were compared favorably to those observed in previous studies related to differences in the genetic character of wheat, the crop calendar, environmental conditions, and other relevant factors. Differences within each region were smaller than differences between regions. More detailed wheat vulnerability curves allow for the assessment of expected wheat yield loss and also allow for a high level of precision in an evaluation, at a variety of scales, of risk of wheat subject to drought. The proposed approach to building regionalized vulnerability curves has the potential to be the basis for crop drought vulnerability curves in different geographical areas at multiple scales.

To improve the riding performance and levitation stability of a high-speed permanent magnet electromagnetic suspension system maglev train, a control strategy based on an integral joint structure model is proposed. First, the system model of joint structure instead of a single maglev unit is established, then a state observer is designed to observe the movement of a suspension frame, and a control strategy based on linear quadratic optimal control method is proposed. This control strategy based on a joint structure model is advantageous in suspension performance in the presence of external disturbances. Comparative simulation results show that this control strategy has a better performance under the condition of external force disturbance. An experiment on single-carriage high-speed permanent magnet electromagnetic suspension system maglev train is conducted on a 1.5-km test line; the performance is satisfactory for employment.

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An easy, but effective, method has been proposed to detect and quantify the Pb(II) in the presence of Cd(II) based on a Bi/glassy carbon electrode (Bi/GCE) with the combination of a back propagation artificial neural network (BP-ANN) and square wave anodic stripping voltammetry (SWASV) without further electrode modification. The effects of Cd(II) in different concentrations on stripping responses of Pb(II) was studied. The results indicate that the presence of Cd(II) will reduce the prediction precision of a direct calibration model. Therefore, a two-input and one-output BP-ANN was built for the optimization of a stripping voltammetric sensor, which considering the combined effects of Cd(II) and Pb(II) on the SWASV detection of Pb(II) and establishing the nonlinear relationship between the stripping peak currents of Pb(II) and Cd(II) and the concentration of Pb(II). The key parameters of the BP-ANN and the factors affecting the SWASV detection of Pb(II) were optimized. The prediction performance of direct calibration model and BP-ANN model were tested with regard to the mean absolute error (MAE), root mean square error (RMSE), average relative error (ARE), and correlation coefficient. The results proved that the BP-ANN model exhibited higher prediction accuracy than the direct calibration model. Finally, a real samples analysis was performed to determine trace Pb(II) in some soil specimens with satisfactory results.

Track irregularities, caused by reasons such as track deformation and installation error, are common in real maglev line. These disturbances brought by tracks exert an adverse influence on the performance of a maglev train levitation system. It takes a lot of maintenance costs and time to keep the tracks in a good condition. In another way, a disturbance rejection through a controller optimization method is proposed in this paper. First, the influences of the track irregularity disturbances on levitation performance are illustrated in detail. Then, an online optimization of the PnP control architecture method is adopted to reject the disturbances caused by track irregularities. The effectiveness of this method is verified through the MATLAB simulation. This method makes the levitation system adaptive to known and unknown time-varying track irregularities, saving time, and human resources. INDEX TERMS Maglev train, online optimization, PnP control architecture, track irregularity.