Here we investigated whether hydrogen can protect the lung from chronic injury induced by hypoxia/re-oxygenation (H/R). We developed a mouse model in which H/R exposure triggered clinically typical lung injury, involving increased alveolar wall thickening, infiltration by neutrophils, consolidation, alveolar hemorrhage, increased levels of inflammatory factors and recruitment of M1 macrophages. All these processes were attenuated in the presence of H2. We found that H/R-induced injury in our mouse model was associated with production of hydroxyl radicals as well as increased levels of colony-stimulating factors and circulating leukocytes. H2 attenuated H/R-induced production of hydroxyl radicals, up-regulation of colony-stimulating factors, and recruitment of neutrophils and M1 macrophages to lung tissues. However, H2 did not substantially affect the H/R-induced increase in erythropoietin or pulmonary artery remodeling. Our results suggest that H2 ameliorates H/R-induced lung injury by inhibiting hydroxyl radical production and inflammation in lungs. It may also prevent colony-stimulating factors from mobilizing progenitors in response to H/R-induced injury.

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A method for parameter identification of the magnetorheological damper (MRD) model with an improved firefly algorithm (IFA) is proposed, and a semi-active seat control system with three-degree-of-freedom (3-DOF) is established by combining with a quarter car model to investigate the ride comfort. The dynamic characteristics of the MRD were analyzed by experimental method. Combined with the IFA, the parameters of the MRD phenomenon model were identified, and the forward model of the MR damper was constructed. The semi-active control model of a 3-DOF seat suspension was established. The MRD controller and suspension system controller were designed. The passive control, PID control, and Fuzzy-PID control on the vibration reduction of the semi-active seat suspension were compared and analyzed, under different road excitation. The simulation results show that the semi-active seat suspension controlled by the PID and Fuzzy-PID can effectively reduce the seat acceleration and dynamic stroke, which significantly improve the ride comfort and operation safety compared to the passive seat suspension.

The design objectives of the structural parameters of the tractor drive motor are diverse, and the constraints are complex. It is difficult to optimize the overall performance of the unit by using the empirical method and single-objective optimization method. This paper proposes a multi-objective optimization method for tractor drive motors based on an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II). Constraints are formulated according to the inherent characteristics of the motor itself and the characteristics of the tractor’s working conditions. The objective function was established with the heat loss of the drive motor and the total efficiency of the drive system. Based on the designed solution process of NSGA-II algorithm, an example optimization was carried out, and the tractor electromechanical drive system was carried out with the single-objective optimization results of the optimal energy use efficiency of the drive motor and the optimal mechanical transmission efficiency of the transmission system as the control group. The test results show that compared with the control group, the proposed multi-objective optimization method can make the overall tractor system efficiency the highest, and the maximum and rated values of the total efficiency ηq of the drive system of the multi-objective optimization design scheme. Compared with the optimal design scheme with ηme as a single objective, it was increased by 2% and 1.4%, respectively, and compared with the optimal design scheme with ηtr as a single objective, it is improved by 26.5% and 73.6%, respectively. It can provide an effective calculation method for the motor design problem in the subsequent development of the tractor electromechanical drive system.

To solve the problems related to the complex structures, multiple parts, and imperceptible assembly quality of combines, this paper compares the performance of the empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), complete ensemble empirical mode decomposition (CEEMD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and information entropy features in the detection data of combine assembly quality, and proposes a vibration detection method of combine assembly quality detection based on multi-entropy feature fusion and optimized least squares support vector machine. Firstly, the vibration signals of the combine are decomposed by various adaptive algorithms, and intrinsic modal function (IMF) components are obtained. Three entropy features are extracted from the components of the modes. The features are visualized by t-distributed stochastic neighbor embedding (t-SNE), and the performance of these entropy features in the combine assembly quality detection is analyzed. Secondly, a feature extraction method based on information entropy fusion is proposed. The optimized kernel principal component analysis (KPCA) is used to fuse and reduce the dimension of the entropy features, and form the fusion features. Finally, the extracted features are imported into optimization least squares support vector machine (LSSVM) model for training to judge the working state and assembly quality problem type of combine. The results show that the accuracy of using the unfused entropy features is 82.5%, the accuracy of the fused features is 87.9%. After WOA optimization, the accuracy of the final classification reaches 92.1%, which is better than 89.6% by GA and 90.5% by PSO. It shows that the method proposed in this paper can accurately identify combine problems with different conditions and can be applied to combine assembly quality detection.