Mangiferin (MG) is an active component in natural medicines, and various studies have been reported on pharmacological effects, but the low solubility and bioavailability of MG limit its wide application. The aim of the present study was to investigate the pharmacokinetic profiles of mangiferin (MG) and mangiferin monosodium salt (MG-Na) in rat plasma by UPLC-MS/MS, which were then compared between the two groups. An appropriate high sensitivity and selectivity ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was applied to the comparison of plasma pharmacokinetics in MG and MG-Na using carbamazepine as internal standard (IS). These results showed that there were statistically significant differences in the pharmacokinetic parameters between MG and MG-Na after a single oral administration at 100 mg/kg. When compared with pharmacokinetic parameters of MG, the AUC(0-t), AUC(0–∞), Cmax,K10, and Ka of MG-Na were increased by 5.6-, 5.7-, 20.8-, 8-, and 83.6-fold, while the Tmax and CL/F were decreased by 4- and 5.7-fold (P<0.001), respectively. t1/2 value showed an increasing trend, but was statistically significant between the two groups. Moreover, the AUC value in the MG-Na group was significantly increased and the relative bioavailability was calculated to be 570% when compared with that of the MG group. These results suggested that the salification reaction of MG can effectively enhance gastrointestinal absorption and relative bioavailability by improving solubility and membrane permeability.

Anomaly detection plays an essential role in health monitoring and reliability assurance of complex system. However, previous researches suffer from distraction by outliers in training and extensively relying on empiric-based feature engineering, leading to many limitations in the practical application of detection methods. In this paper, we propose an unsupervised anomaly detection method that combines random convolution kernels with isolation forest to tackle the above problems in equipment state monitoring. The random convolution kernels are applied to generate cross-dimensional and multi-scale features for multi-dimensional time series, with combining the time series decomposing method to select abnormally sensitive features for automatic feature extraction. Then, anomaly detection is performed on the obtained features using isolation forests with low requirements for purity of training sample. The verification and comparison on different types of datasets show the performance of the proposed method surpass the traditional methods in accuracy and applicability.

A high-quality and secure touchdown run for an aircraft is essential for economic, operational, and strategic reasons. The shortest viable touchdown run without any skidding requires variable braking pressure to manage the friction between the road surface and braking tire at all times. Therefore, the manipulation and regulation of the anti-skid braking system (ABS) should be able to handle steady nonlinearity and undetectable disturbances and to regulate the wheel slip ratio to make sure that the braking system operates securely. This work proposes an active disturbance rejection control technique for the anti-skid braking system. The control law ensures action that is bounded and manageable, and the manipulating algorithm can ensure that the closed-loop machine works around the height factor of the secure area of the friction curve, thereby improving overall braking performance and safety. The stability of the proposed algorithm is proven primarily by means of Lyapunov-based strategies, and its effectiveness is assessed by means of simulations on a semi-physical aircraft brake simulation platform.

In this paper, a nonlinear model predictive control (NMPC) method based on mixed slipdeceleration (MSD) with runway identification is proposed to prevent the aircraft wheels from locking up and improve the braking performance under time-varied runway conditions. The MSD control algorithm reduces the dependence of control performance on slip rate estimation accuracy and retains a good slip rate control performance. The proposed NMPC control method guarantees optimal braking torque on each wheel by individually controlling the slip rate of each wheel near the optimal point. A nonlinear brake control model based on aircraft ground taxiing dynamics is derived. In this model, the tire-runway friction coefficient-slip rate model under different runway conditions and vertical force variation caused by brake are considered. A runway identification algorithm based on friction coefficient and friction coefficient slope is used to identify the real-time runway status, based on which the prediction model and optimization function of the proposed control scheme are modified. The wheel slip stable zone and the system maximum brake torque are regarded as time-domain constraints of the NMPC for safety considerations and physical limitations. The control objectives of the NMPC include longitudinal deceleration, braking performance, and preservation of crew comfort. The proposed MSD-based NMPC controller is verified by a tricyclegeared aircraft model using MATLAB/Simulink software. Simulation results of different control schemes on a specific mixed runway show good performances of the proposed control method. The proposed control method provides a new efficient solution for aircraft wheel braking on variable runway. INDEX TERMS NMPC, mixed slip-deceleration control, aircraft brake control, runway identificationThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

In order to deal with strong nonlinearity and external interference in the braking process, this paper proposes a robust self-learning PID algorithm based on particle swarm optimization, which does not depend on a precise mathematical model of the controlled object. The self-learning function is used to adapt to the diversity of the runway road surface friction, the particle swarm algorithm is used to optimize the rate of self-learning, and robust control is used to deal with the modeling uncertainty and external disturbance of the system. The convergence of the control strategy is proved by theoretical analysis and simulation experiments. The superiority and accuracy of the method are verified by NASA ground test results. The simulation results shows that the adverse effect of the external disturbance is suppressed, and the ideal trajectory is tracked.