The yak manure based biochar was produced at different temperatures of 300, 500 and 700 ℃ held for 3 h, which was characterized by BET surface area, X-ray diffraction, Fourier transform infrared spectroscopy, pH measurement, analysis, scanning electron microscopy and ultimate analysis. The resultant biochar had characteristics of high surface area, high pH, porous structure and rich nutrients such as N, P, Ca, Mg, and K, inferring that the yak manure-derived biochar could be used as a soil conditioner. The field experiment was conducted to study the effect of yak manure derived biochar amendment on the yield and biological traits of highland barley, revealing that adding biochar to soil could increase the yield and growth of highland barley in short-term although the long-term benefits remain to be quantified. The present results can be useful to fill the knowledge gap regarding the potential of yak manure derived biochar to soil improvement.

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With their advantages of high experimental safety, convenient setting of scenes, and easy extraction of control parameters, driving simulators play an increasingly important role in scientific research, such as in road traffic environment safety evaluation and driving behavior characteristics research. Meanwhile, the demand for the validation of driving simulators is increasing as its applications are promoted. In order to validate a driving simulator in a complex environment, curve road conditions with different radii are considered as experimental evaluation scenarios. To attain this, this paper analyzes the reliability and accuracy of the experimental vehicle speed of a driving simulator. Then, qualitative and quantitative analysis of the lateral deviation of the vehicle trajectory is carried out, applying the cosine similarity method. Furthermore, a data-driven method was adopted which takes the longitudinal displacement, lateral displacement, vehicle speed and steering wheel angle of the vehicle as inputs and the lateral offset as the output. Thus, a curve trajectory planning model, a more comprehensive and human-like operation, is established. Based on directional long short-term memory (Bi–LSTM) and a recurrent neural network (RNN), a multiple Bi–LSTM (Mul–Bi–LSTM) is proposed. The prediction performance of LSTM, MLP model and Mul–Bi–LSTM are compared in detail on the validation set and testing set. The results show that the Mul–Bi–LSTM model can generate a trajectory which is very similar to the driver’s curve driving and have a preferable generalization performance. Therefore, this method can solve problems which cannot be realized in real complex scenes in the simulator validation. Selecting the trajectory as the validation parameter can more comprehensively and intuitively reflect the simulator’s curve driving state. Using a speed model and trajectory model instead of a real car experiment can improve the efficiency of simulator validation and lay a foundation for the standardization of simulator validation.

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