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Background To investigate the correlation between Claudin-18 expression and the clinicopathological features and prognosis of gastric cancer. Methods A total of 63 patients who underwent gastric cancer surgery from December 2012 to June 2013 were recruited as the research participants. The clinicopathological data and prognostic information of the participants were collected, and expression levels of Claudin-18 in gastric cancer and adjacent tissues were detected by immunohistochemical (IHC) staining. The correlation between Claudin-18 expression and clinicopathological features of gastric cancer patients was analyzed. The Cox regression model was used to analyze the risk factors associated with the prognosis of gastric cancer patients. Results The expression of Claudin-18 was positive in 35 (55.6%) of the participants' gastric cancer tissues, which was significantly lower than that in the adjacent tissues (51 tissues/81.0%), and the difference was statistically significant (P=0.002). In addition, the IHC staining score of Claudin-18 expression in gastric cancer tissues (1.49±1.69), was significantly lower than that in the adjacent tissues (4.61±1.81), and the difference was statistically significant (P=0.016), The incidence of nerve invasion in patients with low expression of Claudin-18 was significantly higher than that in patients with high expression of Claudin-18 (P=0.038). In addition, univariate Cox regression analysis showed that nerve invasion, Claudin-18 staining score, tumor size, and positive count of lymph nodes were risk factors associated with the survival of gastric cancer patients. Multivariate Cox regression analysis showed that Claudin-18 staining score and tumor size were independent risk factors associated with the survival of gastric cancer patients. The overall survival (OS) of patients with low Claudin-18 staining score or larger tumor size was significantly reduced. Conclusions The low expression of Claudin-18 was closely related to nerve invasion in gastric cancer, which indicated the poor clinical prognosis of gastric cancer patients.

The lateral stability control of tractor semi-trailer plays a vital role for enhancing its driving safety, and the distributed electric drive structure of a hub motor creates opportunities and challenges for realising the lateral stability accurately. Based on the dynamics simulation software TruckSim, a nonlinear dynamic tractor semi-trailer model is established, and a MATLAB/Simulink linear three-degree-of-freedom monorail reference model is established. The upper controller adopts fuzzy proportional–integral–derivative control to export active yaw torque values of the tractor and semi-trailer. The lower controller outputs the driving/braking torque of each wheel according to the target wheel driving/braking rules and torque distribution rules. The tractor produce an active yaw torque through conventional differential braking the hub motor is installed on both sides of the semi-trailer, and the active yaw torque is produced by the coordinated control of the driving/braking torque of the hub motor and the differential braking of the mechanical braking system. To prevent wheel locking, the slip rate of each wheel is controlled. Finally, based on the TruckSim–MATLAB/Simulink cosimulation platform, cosimulation is performed under typical working conditions. The simulation results show that the control strategy proposed in this report is superior to the conventional differential braking control (ESP). It can not only improve the lateral stability of the vehicle more effectively, but also improve the roll stability.

High voltage circuit breaker is a critical equipment of power system. It is very important to ensure the circuit breaker to operate in a normal state. According to statistics, most defect and fault of high voltage circuit breaker is caused by mechanical faults. In this research, the sound and current signals were collected in the simulation experiment of typical mechanical faults, namely iron core jam, two kinds of tripping mechanism faults, and spring fatigue. Then the signals were down sampled, flipped and stacked to fit deep learning model. A convolution neural network (CNN) model consisting eight layers was developed to extract features and categorize faults from the pre-processed signals. The results indicate that the mechanical fault diagnosis accuracy rate is up to 94%, higher than conventional methods using sound or current signal.