As a hard-to-machine material, titanium alloy has a combination of superior heat resistance, corrosion resistance, and mechanical properties, and this leads to poor machinability. High cutting forces and cutting temperature greatly accelerate tool wear during the cutting of titanium alloy frequently. In this article, the ultrasonic vibration turning method was adopted to reduce the wear of diamond tool in the turning of TC4 titanium alloy. Ultrasonic vibration turning tests were carried out in a variety of cutting parameters. The experimental results indicated that the wear of diamond tool was significantly reduced during the turning of TC4 using the ultrasonic vibration turning. In the ultrasonic vibration machining tests, the tool vibration amplitude and frequency are the primary and most influential factors on tool wear, followed by the feed rate and then the cutting depth.

Vandetanib, a multikinase inhibitor, is a target of drug treatments for non-small cell lung cancer (NSCLC). However, phase II and III clinical trials have not conclusively demonstrated the curative effects of vandetanib for NSCLC and the reasons for this are unknown. In the present study, we use the NSCLC cell line Calu-6 as a model to determine the cellular and biological effects of vandetanib. Our results demonstrate that vandetanib impairs Calu-6 cell migration and invasion. We find that vandetanib can directly inhibit RET activity, which influences the Rho-JNK pathway. Overexpression of a constitutively active Rho GTPase antagonizes the inhibitory effects of vandetanib on Calu-6 cells invasion and JNK pathway activation. In addition, vandetanib induces autophagy by increasing the level of reactive oxygen species (ROS) in Calu-6 cells and blockade of autophagy or ROS effectively enhances the cell death effect of vandetanib. In this study, we find vandetanib is of a double effect in some NSCLC cells, presenting new possibilities for the pharmacological treatment of NSCLC and introducing a novel role for vandetanib in treatment options.

Prognostics and health management technology (PHM), a measure to ensure the reliability and safety of the operation of industrial machinery, has attracted attention and application adequately. However, how to use the monitored information to evaluate the degradation of rolling bearings is a significant issue for its predictive maintenance and autonomic logistics. This work presents a reliable health prognosis approach to estimate the health indicator (HI) and remaining useful life (RUL) of rolling bearings. Firstly, to accurately capture the degradation process, a novel health index (HI) is constructed based on correlation kurtosis for different iteration periods and a Gaussian process latency variable model (GPLVM). Then, a multiple convolutional long short-term memory (MCLSTM) network is proposed to predict HI values and RUL values. Finally, we perform experimental datasets of rolling bearings, demonstrating that the presented method surpasses other state-of-the-art prognosis approaches. The results also confirm the feasibility of the presented method in industrial machinery.