Although several approaches including surgery, chemotherapy, and radiotherapy had been applied in the treatment of ESCC, more effective targeted chemotherapies are required to increase the survival rates of patients. This study suggested that inhibiting NF-κB signalling pathway could be an effective approach for the treatment of ESCC. Pristimerin, a potent NF-κB inhibitor, exerted potent anti-ESCC effects both in vitro and in vivo, which may be a promising therapeutic agent for ESCC.
For the layer rubber spring of a high-speed bogie, lab tests were conducted to obtain the dynamic parameters under extremely high and low temperatures (-60°C∼60°C) to obtain the nonlinear stiffness and damping parameters relevant to the frequency, amplitude and temperature changes. It starts from introducing the rubber element dynamic parameter test equipment, test plan and test methods. The applied loads and the temperatures are discussed. Then both the static and dynamic tests were performed for the layer spring of axle box, along the axial and radial directions. It shows that the common results can only reflect frequency- and amplitude-dependent characteristics of rubber, but the corresponding parameter change associated with each frequency and amplitude is highly dependent on the temperature. This phenomenon is quite severe in the low and extremely low temperature cases. Therefore, it is essential and necessary to carry out environmental temperature tests as well as basic theory study and simulations on all rubber components in order to know the parameter variation domain of the suspension system of a railway vehicle.
To control the welding residual stress and deformation of metal inert gas (MIG) welding, the influence of welding process parameters and preheat parameters (welding speed, heat input, preheat temperature, and preheat area) is discussed, and a prediction model is established to select the optimal combination of process parameters. Thermomechanical numerical analysis was performed to obtain the residual welding deformation and stress according to a 100 × 150 × 50 × 4 mm aluminum alloy 6061-T6 T-joint. Owing to the complexity of the welding process, an optimal Latin hypercube sampling (OLHS) method was adopted for sampling with uniformity and stratification. Analysis of variance (ANOVA) was used to find the influence degree of welding speed (7.5–9 mm/s), heat input (1500–1700 W), preheat temperature (80–125 °C), and preheat area (12–36 mm). The range of research parameters are according to the material, welding method, thickness of the welding plate, and welding procedure specification. Artificial neural network (ANN) and multi-objective particle swarm optimization (MOPSO) was combined to find the effective parameters to minimize welding deformation and stress. The results showed that preheat temperature and welding speed had the greatest effect on the minimization of welding residual deformation and stress, followed by the preheat area, respectively. The Pareto front was obtained by using the MOPSO algorithm with ε-dominance. The welding residual deformation and stress are the minimum at the same time, when the welding parameters are selected as preheating temperature 85 °C and preheating area 12 mm, welding speed is 8.8 mm/s and heat input is 1535 W, respectively. The optimization results were validated by the finite element (FE) method. The error between the FE results and the Pareto optimal compromise solutions is less than 12.5%. The optimum solutions in the Pareto front can be chosen by designers according to actual demand.
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