ABSTRACT This study aims to explore the relationship between the anxious symptoms and the impairment of 5-hydroxytryptamine system in PD mice induced by different dosages of MPTP. The mice from the three model groups, the low-dose, dose and high-dose group, took longer time in the dark box than those in the control group (P<0.05). However, no statistically significant differences were found among the model groups. The number of open arm entry (OE) and the open arm time (OT) were significant lower in the model group than those in the control group in the elevated plus-maze test (P＜0.05). The percentage of OE in modle group was significantly lower compared with the control group (P＜0.05). The concentrations of striatum DA, HVA, 5-HT, and 5-HIAA were significantly reduced in the three model groups compared to the control group (P＜0.05). The 5-HT concentrations of high-dose group was significantly lower than those of the control group in the prefrontal cortex (P＜0.05). Anxiety symptoms were appeared in the three model groups of early PD mice, but no difference existed among these groups. The 5-hydroxytryptamine system was damaged after MPTP injection, which could lead to anxiety. However, the impairment of 5-hydroxytryptamine system induced by MPTP was dose-independent.

The hot deformation behaviours of Ti-6Al-4V-0.1Ru alloy were investigated by isothermal hot compression tests in the temperature range of 1023-1423 K and strain rate range of 0.01-10 s-1. The β transus was determined to be 1198 K by continuous heating method. The values of deformation activation energy Q at the strain of 0.3 were calculated to be 630.01 kJ/mol in dual-phase field and 331.75 kJ/mol in β-phase field. Moreover, the processing maps at the strain of 0.2, 0.4, 0.6 and 0.8 were developed based on dynamic materials model (DMM). To deeply understand the microstructure evolution mechanism during hot deformation processes and to verify the processing maps, the microstructures at different deformation conditions were observed. The stable microstructures (i.e. globularization, dynamic recovery (DRV) and β dynamic recrystallization (β-DRX)) and instable microstructures (i.e. lamellae kinking and flow localization) were obtained. To make it useful in the design of industrial hot working schedules for this material, a microstructural mechanism map was constructed on the basis of processing maps and microstructure observation. Deformation conditions in the vicinity of 1150 K & 0.01 s-1 where globularization occurs and in the vicinity of 1323 K & 0.01 s-1 where β-DRX occurs are recommended.

Hot compression tests of as-cast Ti-6Al-2Zr-1Mo-1V alloy in a wide temperature range of 1073-1323 K and strain rate range of 0.01-10 s-1 were conducted by a servo-hydraulic and computer-controlled Gleeble-1500 machine. The hot flow behaviors of Ti-6Al-2Zr-1Mo-1V alloy show highly non-linear relationships with strain, strain rate and temperature. In order to accurately and effectively characterize the complex flow behaviors, support vector regression (SVR) which is a machine learning method was combined with Genetic Algorithm (GA) to characterize the flow behaviors, namely, the GA-SVR. The study abilities, generation abilities, and modeling efficiencies of the improved Arrhenius-type constitutive model, ANN, and GA-SVR for flow behaviors of as-cast Ti-6Al-2Zr-1Mo-1V alloy were detailedly compared. Comparison results show that the study ability of the GA-SVR is as strong as the ANN. The generation abilities and modeling efficiencies of these models were shown as follows in ascending order: the improved Arrhenius-type constitutive model < ANN < GA-SVR. Based on the established GA-SVR, the continuously three-dimensional relationships among flow stress, temperature, strain, and strain rate were constructed, which improve the simulation accuracy and related research fields where stress-strain data play important roles.

Hot compressions of as-cast AZ80 magnesium alloy in a wide temperature range of 523-673 K and strain rate range of 0.01-10 s–1 with a height reduction of 60% were conducted by a Gleeble-1500 thermo-mechanical test simulator. The hot flow behaviors show highly non-linear intrinsic relationships with temperature, strain and strain rate. In order to model the complicated flow behaviors, error back-propagation algorithm, a representative method to minimize the target error, was selected to train the artificial neural network. A comparative study was made on the predictabilities of the improved Arrhenius-type and BP-ANN model by using two standard statistical parameters including correlation coefficient (R) and average absolute relative error (AARE). Comparison results show that the well-trained BP-ANN has higher prediction accuracy. Three highlight applications were presented. Firstly, the strain-stress data volume was expanded by BP-ANN predictions above experimental conditions. Secondly, the expanded data were applied in the simulations of isothermal compressions, and high simulation accuracy for the load-stroke curve was achieved. Thirdly, a three-dimensional (3D) interaction space of stress, strain, temperature and strain rate was constructed based on the intensive data, which supplies the stress data to arbitrary temperature, strain rate, and strain.