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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.

The continual transmission of epidemics makes health workers aware of the importance of airborne transmission in special cases. The well-known equation Wells-Riley highlights the importance of ventilation to dilution of infectious aerosol as well. And the airborne transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been confirmed. The main problem of the coronavirus disease 2019 (COVID-19) epidemic of three families in a restaurant in Guangzhou lies in unreasonable ventilation. An air-conditioning system of Ceiling radiant cooling plate (CRCP) combined with a dedicated outdoor air system (DOAS) has been proposed in this paper. The simulation of the diffusion of tiny exhaled droplets is carried out through SF6 tracer gas. A numerical simulation of the original indoor air distribution of the restaurant and that of the new air-conditioning system has been conducted. Pollutant concentrations of different indoor locations and exposure indexes under four working conditions of ventilation rates 3, 6, 9, and 12 ACH are calculated. The results suggest that the air supply mode of air conditioners put forward in this paper can decrease the risk of indoor infection to a large extent. When the ventilation rate is 1L/s, the probability of infection is very high. With the increase in ventilation times, the risk of infection gradually decreases. Offering suitable ventilation in crowded spaces can greatly prevent and control the air transmission of coronavirus.

Abstract Background The chemokine receptors CXCR4 and CCR7 play an important role in cancer invasion and metastasis. This study investigated the expression of CXCR4, CCR7, CXCL12, CCL21, and EGFR to illustrate the role of these biomarkers in breast cancer metastasis and prognosis. Methods The CXCR4, CCR7, CXCL12, CCL21, and EGFR biomarkers were analyzed along with ER, PR, and HER-2/neu in breast cancer tissue microarray (TMA) specimens, including 200 primary breast cancer specimens by immunohistochemistry. Corresponding lymph nodes from the same patients were also examined using the same method. Results Together with their CXCL12 and CCL21 ligands, CXCR4 and CCR7 were significantly highly expressed in tumor cells with lymph node (LN) metastasis. Similarly, EGFR was expressed highly in tumors with LN metastasis. The ligands were especially expressed in metastatic tumors than in primary tumors from the same patients. Moreover, the expression of both CXCR4 accompanied by CCR7 and CXCL12 accompanied by CCL21 were up-regulated. Kaplan-Meier survival analysis revealed that patients exhibiting high CXCR4, CCR7, and EGFR expression experienced a shorter survival period compared with those with low expression. Conclusions The expression of CXCR4, CCR7, and EGFR may be associated with LN metastasis. Moreover, the expression of these receptors can serve as an indicator of undesirable prognosis in patients with breast cancer.

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]