The sea-crossing railway bridge is exposed to a high risk of wind and wave, which threatens the safety of the bridge and railway. A wind–wave–vehicle–bridge dynamic analysis model for sea-crossing railway bridge under wind and wave loadings is developed by extending the previous wind–vehicle–bridge model. The developed wind–wave–vehicle–bridge model involves multipoint fluctuating wind field, irregular wave field, finite element model of the bridge, and mass–spring–damper model of the vehicle. The correlation between wind and wave is considered by an empirical curve derived based on field measurement. Static, buffeting, and self-excited wind forces on the bridge and vehicle are considered with coefficients obtained from wind tunnel tests. The wave forces on the bridge are calculated by Morison equation including stretching modification. The governing equations of the wind–wave–vehicle–bridge model are solved in time domain by Newmark-β method to compute the dynamic response of bridge and vehicle. The dynamic response of bridge and vehicle is compared and discussed in both wind–wave–vehicle–bridge and wind–vehicle–bridge model. The performance of bridge and vehicle are finally evaluated. Studies of dynamic response under correlated wind and wave are found to be imperative for assessment of structural and vehicle safety and driving comfort of sea-crossing railway bridge.

In order to explore an easy way determining the hard particle distribution in the cold sprayed composite coatings. Four Al5056/SiC composite coatings were employed in the present study. One quantitative metallographic analysis method, box-counting (BC) and hardness indentation method, the Weibull distribution were used. The relative deviation value σ and the shape parameter m were used as the quantitative factors for determining the uniformity of the SiC distribution in the coating, respectively. Results show that both methods obtained the same tendency that the uniformity of SiC distribution in the coating following the order of Al5056 + 30vol.-%SiC> +15vol.-% SiC> +45vol.-%SiC> +60vol.-%SiC. Results suggest that the increase of the SiC content could worsen the SiC uniformity in the composite coatings. However, there is still an optimised SiC content for uniform distribution, as for the 30vol.-% for the present case. The hardness Weibull distribution is more simple and feasible than the BC method.

Background: We aimed to explore the ole and mechanism of lactate receptor (HCAR1) in the angiogenesis of leptomeningeal fibroblast-like cells. Methods: Human brain fibroblast-like cells were selected and some cells were deactivated, analyzed and compared with HCAR1 mRNA and protein expressions in deactivated/normal cells. HCAR1-/- mice and wild type (WT) mice were selected and divided into WT, WT exercise, HCAE1 KO and HCAE1 KO exercise groups, with 10 mice for each group. HCAR1mRNA and expression levels of proteins in fibroblast-like cells, mRNA and expression levels of proteins in Collagen IV, phosphatidylinositol trihydroxykinase (PI3K), serine threonine kinase (AKT) and extracellular signal-regulated kinases 1 and 2 (ERK1/2) in hippocampus were compared, and the microvessel density (MVD) and diameter were calculated. Results: mRNA and expression levels of proteins in Collagen IV, PI3K, AKT, ERK1/2 and MVD in hippocampus were significantly higher in the WT exercise group than those in the WT group, microvessel diameter was significantly lower than that in the WT group (P<0.05). mRNA and expression levels of proteins in Collagen IV, PI3K, AKT, ERK1/2 and MVD in hippocampus in the HCAR1 KO and HCAR1 KO exercise groups were significantly lower than those in the WT group, microvessel diameter was higher than that in the WT group (P<0.05). Compared with the HCAR1 KO exercise group, the changes of mRNA in Collagen IV, PI3K, AKT, ERK1/2 and microvascular were not significant. Conclusion: Exercise can promote cerebral angiogenesis through the activation of the lactate receptor HCAR1 and the ERK1/2-PI3K/Akt signaling pathways.

In order to explore the main factors influencing the thermal conductivity of the cold sprayed Cu coating, four sets of Cu coatings with different deformation and annealing conditions were prepared by high/middle pressure cold spray systems followed by the annealing procedure. Microstructure and recrystallization analysis of the coatings were characterized by SEM and EBSD. Relationship of the thermal conductivity of the coating with the porosity and particle deformation was discussed. Results show that porosity is the main factor influencing the thermal conductivity of the Cu coating. The decrease in porosity by high pressure impacting of particles can improve the conductivity of the coating, while the annealing process can further decrease the porosity by increasing the annealing temperature and meanwhile eliminate the grain boundary by grain growth and recrystallization, which makes the annealing process an effective way to improve the conductivity of the cold sprayed Cu coating.

Five hard-phase aluminium-based composite coating types with volume fractions of 12.5%, 25%, 37.5%, and 50%, respectively, were used. The addition of a large hard phase enhanced the coating densification. An increase in hard-phase content in the coating distributes more uniformly, but when the hard-phase content is too high, the hard phase is more likely to agglomerate, which decreases the distribution uniformity. Therefore, all those with a 25% volume fraction had the best uniformity. The kinetic energy of the hard-phase particles affects the distribution uniformity by changing the deposition method. When the kinetic energy is small, particles cannot be deposited effectively (Al 2 O 3 -8). When the kinetic energy is large, the metal-based plastic flows to form a fracture belt, which reduces the distribution uniformity (WC-72). When the kinetic energy does not cause the deposition method to change, the uniformity of the hard-phase distribution is similar.