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This study tested a holistic model that investigated the interaction effect of negative mentoring experiences and moqi (pronounced “mò-chee”) with a mentor—where moqi refers to a situated state between two parties in which one party understands and cooperates well with the other party without saying a word—on the protégés’ turnover intention, along with the mediating role of protégés’ harmonious work passion.

Data were collected from 281 protégés through a three-wave questionnaire survey with a 1-month lag between waves. We used a hierarchical multiple regression and bootstrapping analysis to test our hypotheses.

Our results support the mediating effect of harmonious work passion on the positive relationship between protégés’ negative mentoring experiences and turnover intention. In addition, our analysis confirmed that moqi with the mentor amplifies both the impact of protégés’ negative mentoring experiences on harmonious work passion and the indirect effect of negative mentoring experiences on protégés’ turnover intention via harmonious work passion.

By demonstrating the interaction effect of protégés’ negative mentoring experiences and moqi with their mentor on turnover intention, as well as the mediating role of harmonious work passion, this study expands our understanding of the mechanism and boundary condition of the effect of negative mentoring experiences and provides inspiration and guidance for mentoring practices.

The accurate prediction of driving torque demand is essential for the development of motion controllers for mobile robots on complex terrains. This paper aims to propose a hybrid model of torque prediction, adaptive EC-GPR, for mobile robots to address the problem of estimating the required driving torque with unknown terrain disturbances.

An error compensation (EC) framework is used, and the preliminary prediction driving torque value is achieved using Gaussian process regression (GPR). The error is predicted using a continuous hidden Markov model to generate compensation for the prediction residual caused by terrain disturbances and uncertainties. As the final step, a gain coefficient is used to adaptively tune the significance of the compensation term through parameter resetting. The proposed model is verified on a sample set, including the driving torque of a mobile robot on three different sandy terrains with two driving modes.

The results show that the adaptive EC-GPR yields the highest prediction accuracy when compared with existing methods.

It is demonstrated that the proposed model can predict the driving torque accurately for mobile robots in an unconstructed environment without terrain identification.

The purpose of this study is to compare the prediction models for half-cell potential (HCP) of RCC slabs cathodically protected using pure magnesium anodes and subjected to chloride ingress.The models for HCP using 1,134 data set values based on experimentation are developed and compared using ANFIS, artificial neural network (ANN) and integrated ANN-GA algorithms.

In this study, RCC slabs, 1000 mm × 1000 mm × 100 mm were cast. Five slabs were cast with 3.5% NaCl by weight of cement, and five more were cast without NaCl. The distance of the point under consideration from the anode in the x- and y-axes, temperature, relative humidity and age of the slab in days were the input parameters, while the HCP values with reference to the Standard Calomel Electrode were the output. Experimental values consisting of 80 HCP values per slab per day were collected for 270 days and were averaged for both cases to generate the prediction model.

In this study, the premise and consequent parameters are trained, validated and tested using ANFIS, ANN and by using ANN as fitness function of GA. The MAPE, RMSE and MAE of the ANFIS model were 24.57, 1702.601 and 871.762, respectively. Amongst the ANN algorithms, Levenberg−Marquardt (LM) algorithm outperforms the other methods, with an overall R-value of 0.983. GA with ANN as the objective function proves to be the best means for the development of prediction model.

Based on the original experimental values, the performance of ANFIS, ANN and GA with ANN as objective function provides excellent results.