No abstract

In the 21st century, with the increasingly urgent requirements for lightweight in the fields of aviation, aerospace, and electronics, especially automobiles, many properties of magnesium alloy materials, especially the low-density performance characteristics, have been widely valued. In order to better use magnesium metal materials, it is very important to evaluate its mechanical properties. This article is based on 196 sets of mechanical performance experimental results and related data of AZ31 and AZ91 2 magnesium alloys. Based on data analysis and sorting, take deformation temperature, deformation rate, deformation coefficient, solid solution temperature, and solid solution time as input and take ultimate tensile strength (UTS), yield strength (YS), and elongation (ELO) as output. The 5-8-1 three-layer BP neural network forecast model optimized by the genetic algorithm is used for data training. The training results show that the prediction model can accurately predict the tensile strength, yield strength, and elongation. Compared with the general BP neural network prediction model, the BP neural network based on the genetic algorithm has small discreteness and high fitness: the average error of UTS and YS of AZ31 magnesium alloy is reduced to 0.88% and 3.3%, respectively. The most obvious is that the elongation of AZ31 ELO is reduced, and the error is reduced to 8.1%.

Osmotic and ionic induced salt stress suppresses plant growth. In a previous study, Enterobacter ludwigii B30, isolated from Paspalum vaginatum, improved seed germination, root length, and seedling length of bermudagrass (Cynodon dactylon) under salt stress. In this study, E. ludwigii B30 application improved fresh weight and dry weight, carotenoid and chlorophyll levels, catalase and superoxide dismutase activities, indole acetic acid content and K+ concentration. Without E. ludwigii B30 treatment, bermudagrass under salt stress decreased malondialdehyde and proline content, Y(NO) and Y(NPQ), Na+ concentration, 1-aminocyclopropane-1-carboxylate, and abscisic acid content. After E. ludwigii B30 inoculation, bacterial community richness and diversity in the rhizosphere increased compared with the rhizosphere adjacent to roots under salt stress. Turf quality and carotenoid content were positively correlated with the incidence of the phyla Chloroflexi and Fibrobacteres in rhizosphere soil, and indole acetic acid (IAA) level was positively correlated with the phyla Actinobacteria and Chloroflexi in the roots. Our results suggest that E. ludwigii B30 can improve the ability of bermudagrass to accumulate biomass, adjust osmosis, improve photosynthetic efficiency and selectively absorb ions for reducing salt stress-induced injury, while changing the bacterial community structure of the rhizosphere and bermudagrass roots. They also provide a foundation for understanding how the bermudagrass rhizosphere and root microorganisms respond to endophyte inoculation.

Traffic resistance of turfgrasses is an essential indicator of urban recreational and sports turf quality (TQ). In our study, four turfgrass species were investigated for their wear resistance. A self-made traffic simulator was used to determine the wear resistance of the study turf area in a 2-year field trial (2019–20). The experimental plots were established using a randomized block design with three replicates. The morphological characteristics, soil physical properties, and physiological indices of the grasses were analyzed. Using the acquired quantitative data, we set the turf cover index (TCI), the turf quality index (TQI), and the shoot density index (SDI) as the wear tolerance index, and assessed the correlations among these morphological characteristics, soil physical properties, physiological indices, and wear tolerance. ‘Lanyin III’ zoysiagrass and ‘Tifgreen’ hybrid bermudagrass provided relatively greater wear tolerance, followed by ‘Qingdao’ zoysiagrass and common bermudagrass after 12 weeks of traffic exposure in 2019 and 2020. Traffic changes the soil physical properties and affects the physiological metabolism of turfgrasses. Leaf morphology characteristics and the mechanical strength of these grasses were related significantly to TCI, TQI, and SDI, and most physiological responses and soil properties correlated significantly with TCI and TQI. Our findings of the correlations among physiological responses, soil properties, leaf morphology, and wear tolerance will allow grass breeders to evaluate their breeding procedures more efficiently.