Search

In order to accurately determine the level of fatigue of coal mine monitoring and dispatching operators and to reduce the error rate of coal mine monitoring and dispatching operations, a simulation experiment of coal mine monitoring and dispatching was carried out using eye tracking technology to collect the operator’s eye movement data and objective and subjective fatigue determination values. The K-means clustering algorithm was used to classify fatigue levels and train a neural network to build a fatigue prediction model for coal mine monitoring and dispatching operations. The results showed that the best fatigue level was classified into three categories and the fitting degree of the neural network prediction model was 90.58%. The prediction model was tested on a coal mine monitoring operation model in Shanxi Province, and the average error of the model field prediction was 6.26%, which was a good prediction effect.

Objective To observe the efficacy and side effects of olanzapine in the treatment of nausea and vomiting caused by opioid analgesics in cancer patients. Methods We randomly divided 85 cancer patients with nausea and vomiting caused by opioid analgesics into two groups. Treatment group was treated with olanzapine tablets and control group received metoclopramide injection solution. The clinical effect and medical side effects were compared between two groups. Results Compared with control group, the treatment group was significantly better in inhibiting nausea, vomiting and reducing opioid discontinuation rate (P < 0.05). Two groups were similar in drowsiness, dizziness, edema and other adverse reactions rate (P > 0.05). The control group showed significantly higher possibility in extrapyramidal reaction (akathisia) than the treatment group (P=0.024), while only one case of tumble happened in treatment group. Conclusion Olanzapine is a good choice in the treatment of nausea and vomiting caused by opioid analgesics and worth extending its clinical application.

To achieve the goals of carbon emission-peak and carbon neutrality, the energy conservation problem of subway air-conditioning system has become more and more prominent. Since the load of the subway equipment management room varies obviously with the start and stop of the equipment, the traditional air-conditioning system of constant air volume cannot change the air supply volume of the system, which would cause the waste of the cooling capacity. The air-conditioning system of variable air volume can be used to achieve the air supply on demand for different rooms at different times. This paper builds a simulation platform of the VAV air-conditioning system for a subway station in Zhengzhou and the platform is integrated with the load model of the subway equipment room to achieve on-demand air supply. The simulation platform is used to simulate the control characteristics and air volume characteristics of the VAV system. By adjusting the valve opening of the VAV-box terminal, the demanded air volume of the room is supplied on according to different load demands. The results show that the actual operating air volume basically matches the demand air volume and the deviation is less than 1%. The control effects of room temperature between the constant air volume system and the VAV system are compared. The room temperature with the constant air volume system is decreased to 34 °C, which is lower than the design temperature of 36 °. While, the room temperature with the VAV system can be precisely controlled at 36 °, and the waste of cooling capacity is avoided. [ABSTRACT FROM AUTHOR]