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Rational hospital human resource allocation planning is important to improve the efficiency of China’s health human resource allocation and reduce the losses caused by staff waste and shortage. In this paper, we take the medical and nursing configuration of a general tertiary hospital in X city as a guiding framework and use inductive and deductive methods to summarize the factors affecting medical and nursing staffing and the experience of management in the previous period. By proposing an adaptive algorithm based on learning rate for improving BP neural network with differentiated learning rate, the dynamic adjustment of weights between different nodes is achieved. Finally, through database design and module design, two functional modules of human resource management and human resource prediction are constructed. The results of the case validation show that the HR demand forecasting model has the best prediction effect for health technicians, and the relative errors are all less than 5%, with an average relative error of 1.23% and a minimum value of only 0.25%. The relative error between the predicted and actual values of the ARIMA (2, 2, 2) dataset for practicing (assistant) physicians is less than 0.005. It shows that the HR model constructed in this paper has a certain quantitative guidance value for the rational planning of human resource allocation for hospital positions.

To improve the homogeneity of heating, the magnetic absorbing material Fe3O4 is considered to use in microwave pyrolysis of oily sludge. Therefore, the effect of Fe3O4 on the microwave pyrolysis of oily sludge is investigated based on gas volatile products. Thermogravimetric mass spectrometry result certifies that Fe3O4 will increase the weight-loss ratio from 13.0% to 14.1%. Also, the characteristic peak intensity of CO in gas products decreases from 5.41 × 10−10 A/g to 1.95 × 10−10 A/g, while H2O increases from 3.57 × 10−10 A/g to 7.32 × 10−10 A/g and CO2 increases from 6.87 × 10−10 A/g to 8.92 × 10−10 A/g. This is caused by the esterification of alcohols and esters and the reduction of Fe3O4 by CO. Based on the decrease in activation energy and enthalpy values of Stage II and IV, it infers that Fe3O4 catalyzes the pyrolysis process of oily sludge to some extent. Similarly, gas chromatography-mass spectrometry results show that Fe3O4 can make the types of gas products increase. Especially, the number of molecular species increases from 5 to 46 under 200–300 °C. Finally, a simple molecular dynamics simulation model is conducted, and the results are in agreement with the experimental results. This study shows that Fe3O4 improves the pyrolysis homogeneity and the pyrolysis efficiency also improves. [ABSTRACT FROM AUTHOR]

The continuous and close combination of artificial intelligence technology and agriculture promotes the rapid development of smart agriculture, among which the agricultural robot navigation line recognition algorithm based on deep learning has achieved great success in detection accuracy and detection speed. However, there are still many problems, such as the large size of the algorithm is difficult to deploy in hardware equipment, and the accuracy and speed of crop row detection in real farmland environment are low. To solve the above problems, this paper proposed a navigation line extraction algorithm for corn spraying robot based on YOLOv8s‐CornNet. First, the Convolution (Conv) module and C2f module of YOLOv8s network are replaced with Depthwise Convolution (DWConv) module and PP‐LCNet module respectively to reduce the parameters (Params) and giga floating‐point operations per second of the network, so as to achieve the purpose of network lightweight. Second, to reduce the precision loss caused by network lightweight, the spatial pyramid pooling fast module in the backbone network is changed to atrous spatial pyramid pooling faster module to improve the accuracy of network feature extraction. Meanwhile, normalization‐based attention module is introduced into the network to improve the network's attention to corn plants. Then the corn plant was located by using the midpoint of the corn plant detection box. Finally, the least square method is used to extract the corn crop row line, and the middle line of the corn crop row line is the navigation line of the corn spraying robot. From the experimental results, it can be seen that the navigation line extraction algorithm proposed in this paper ensures both the real‐time and accuracy of the navigation line extraction of the corn spraying robot, which contributes to the development of the visual navigation technology of agricultural robots. [ABSTRACT FROM AUTHOR]