No abstract

: Recognition for proteins is essential for study of biology. In order to obtain the function proteins of Elymus nutans, we sequenced their transcriptomes in Inner Mongolia of China. Then, we used BLAST software for their function annotations. Besides, we used machine learning methods to recognize proteins which are not annotated by the software. In the process, we focused on identify the proteins with binding functions. In our research, features are extracted by four algorithms and selected by mutual information estimator. Meanwhile, a total of three types of classifiers are constructed based on K-nearest neighbor algorithm and gradient boosting algorithm. Results show that there are 848 proteins with ATP binding function, 113 proteins with heme binding function, 315 proteins with zinc-ion binding function, 135 proteins with GTP binding function and 21 proteins with ADP binding function. Furthermore, we have successfully predicted the functions of 10 special protein sequences whose function annotations cannot be obtained by making sequence alignment with seven famous protein databases. Among them, seven sequences have ATP binding functions, one sequence has heme binding function, one sequence has zinc-ion binding function and the other one has GTP binding function.

As a unique device of railway networks, the normal operation of switch machines involves railway safe and efficient operation. Predictive maintenance becomes the focus of the switch machine. Aiming at the low accuracy of the prediction state and the difficulty in state visualization, the paper proposes a predictive maintenance model for switch machines based on Digital Twins (DT). It constructs a DT model for the switch machine, which contains a behavior model and a rule model. The behavior model is a high-fidelity visual model. The rule model is a high-precision prediction model, which is combined with long short-term memory (LSTM) and autoregressive Integrated Moving Average model (ARIMA). Experiment results show that the model can be more intuitive with higher prediction accuracy and better applicability. The proposed DT approach is potentially practical, providing a promising idea for switching machines in predictive maintenance.