Prediction of Disease Transmission Risk in Universities Based on SEIR and Multi-hidden Layer Back-propagation Neural Network Model

Against the background of regular epidemic prevention and control,in order to ensure the return of teachers to work,students to return to school and safe operation of schools,the risk of disease transmission is analyzed in key areas such as university canoons,auditoriums,teaching buildings and dormitories.The risk model of epidemic transmission in key regions of universities is established based on the improved SEIR model,considering the four groups of people,namely susceptible,latent,infected and displaced,and their mutual transformation relationship.After feature post-processing,the selected feature parameters are processed with monotone non-decreasing and smoothing,and used as noise-free samples of stacked sparse denoising automatic coding network to train the network.Then,the feature vectors after dimensionality reduction of the stacked sparse denoising automatic coding network are used as the input of the multi-hidden layer back-propagation neural network,and these features are used as tags to carry out fitting training for the network.The results show that the implementation of control measures can reduce the number of contacts between infected people and susceptible people,reduce the transmission rate of single contact,and reduce the peak number of infected people and latent people by 61%and 72%respectively,effectively controlling the disease spread in key regions of universities.Our method is able to accurately predict the number of infections.

Laboratory investigation of coal sample permeability under the coupled effect of temperature and stress

The stress and temperature sensitivities of coal reservoirs are critical geological factors affectingcoalbed methane(CBM)well exploitation;in particular it is important to reduce or eliminate their influence on coal reservoir permeability.To investigate coal permeability behavior at various effective stresses and temperatures,CH4 permeability tests were conducted on raw coal samples under a varying effective stress of 2.0-8.0 MPa under five different temperatures(25℃-65℃)in the laboratory.The results show that the permeability of the coal samples exponentially decreases with increasing effective stress or temperature,which indicates obvious stress and temperature sensitivity.Through a dimension-less treatment of coal permeability,effective stress,and temperature,a new stress sensitivity index S and tempera-ture index ST are proposed to evaluate coal stress and temperature sensitivity evaluation parameters.These new parameters exhibit integrality and uniqueness,and,in combination with stress sensitivity coefficient αk,tempe-rature sensitivity coefficient αT,and the permeability damage rate PDR,the sensitivities of coal permeability to stress and temperature are evaluated.The results indicate that coal sample stress sensitivity decreases with increasing effective stress,while it first decreases and then increases with increasing temperature.Additionally,coal sample temperature sensitivity shows a downward trend when temperature increases and fluctuates when effective stress increases.Finally,a coupled coal permeability model considering the impacts of effective stress and temperature is established,and the main factors affecting coal reservoir permeability and their control mechanism are explored.These results can provide some theoretical guidance for the further development of deep CBM.