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This paper constructs a PLS structural equation model based on structural equation modeling and PLS computational methods. The focus is on the expression of the measurement model and the application steps while analyzing the general measurement model of structural equations. The PLS method is applied to solve the structural equation model to achieve the best summary of information in the system of independent variables. Combining the structural equation modeling of two simple variables, the structural equation modeling based on PLS was designed to analyze the path of influence of professional mindset on job performance. The results show that a positive teaching mindset can somewhat improve job performance. The z-value of the total effect of professional mindset on innovative performance was 7.798, and the upper and lower limits of the Boolstrap’s bias-corrected 95% confidence interval and percentile 95% confidence interval ranged from (0.751,1.229) and (0.745,1.237), respectively.

This paper aims to propose a real-time detection method of wheel-rail conditions in a monorail vehicle-track nonlinear system based on vehicle vibration signals. Monorail rubber tires exhibit strong nonlinearity and three-dimensional elasticity, resulting in highly coupled vertical and lateral vibrations. As a result, traditional methods applied in railway system such as transfer function analysis are not suitable for back-calculation of road irregularities for this nonlinear system. Iterative simulation with numerous parameters is time-consuming, thus this paper proposes a parallel simulation approach fused with the genetic algorithm to shorten the calculation time and facilitate big data processing. The multi-rigid body model's simulation result can closely match the test data by intelligently modifying the vehicle parameters. This method overcomes the transfer function's limitations in nonlinear systems and the significant errors introduced by the simplified mathematical derivation method. It also overcomes the shortcoming of significant errors in the mathematical derivation method and disperses errors caused by simplifying the multi-rigid body dynamics and ensures calculation accuracy. Additionally, this paper highlights the application of artificial intelligence techniques in intelligent wheel eccentricity detection. It improves the traditional algorithm to shorten the calculation time and benefit big data processing. [ABSTRACT FROM AUTHOR]

Aiming to address the issue of significant artefact interference in conventional reverse time migration (RTM) imaging under zero-time imaging conditions of ground-penetrating radar (GPR), this study proposes an improvement of RTM using cross-correlation superposition projection (CSP) to enhance imaging quality. The method is based on the original RTM imaging framework, where all single-trace signals are individually processed using RTM. The individual imaging results are then multiplied pairwise and added together to create a CSP. After normalizing the CSP and properly setting the adaptive threshold, the CSP can focus RTM imaging on the GPR signal reflection interface area, effectively removing artefact interference. Synthetic data test confirms that the CSP-improved RTM, when compared to conventional RTM and Laplace filtering methods, not only preserves all effective information in imaging but also eliminates artefact interference. This improvement significantly enhances the quality and resolution of the imaging results. Furthermore, the practicality and effectiveness of the CSP-improved RTM method in engineering applications have been validated using a set of measured GPR data. [ABSTRACT FROM AUTHOR]