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To discuss the value of dual-source CT dual-energy parameters combined with serum detection of vascular endothelial growth factor (VEGF) and carcinoembryonic antigen (CEA) in the diagnosis of early lung cancer (LC). In total, 100 patients with lung lesions in our hospital from January 2020 to January 2022 were selected for retrospective study, and were divided into the lung cancer group (group A) and benign lung disease group (group B) according to the final results of pathological diagnosis, using dual-source CT dual-energy scanning combined with serum detection of VEGF and CEA to analyze the diagnostic values of single detection and combined detection. Among the 100 patients with lung lesions, there were 58 patients with LC and 42 patients with benign lung diseases after pathological examination, with no statistical difference in normalized iodine concentration (NIC) and the increased value of iodine at arterial phase between the two groups (P > 0.05). The NIC value of group A was higher than group B at venous phase (P < 0.05). The serum levels of VEGF and CEA in group A were higher than group B (P < 0.05). The area under the curve, specificity, sensitivity, Youden index and 95% CI of combined diagnosis were higher than single detection of NIC, VEGF and CEA at venous phase. The combined application of dual-source CT dual-energy parameters and serum detection of VEGF and CEA has higher diagnostic value in patients with early LC, which can provide effective reference for clinical diagnosis and treatment, with higher application value in clinic. [ABSTRACT FROM AUTHOR]

In order to solve the safety problems that exist in traditional charging technology, a new visible light-curing 3D printing platform and energetic slurry formula were designed. Within them, the formula of light-curing slurry is 60.46% EA/PUA (prepolymer), 37.21% PETA/HDDA/BA (active diluent monomer), 0.47% CQ (visible light initiator), 0.93% DMT (curing accelerator), and 0.93% additive. The curing time of visible light-curing paste is 7.5 s, the viscosity is 22 6 mPa·s, the curing hardness is 2 H, the gel rate is 96.67%, the shrinkage rate is 98.35%, and the curing depth is 9.41 mm. The optimal formulation of energetic slurry is 20% visible light-curing slurry, 57.72% AP, 15.28% Al and 7% RDX. The 3D printing energetic powder column has a smooth surface, uniform filler distribution, a flat surface, no protrusions or other defects, and good matching. The average burning rate is 1.165 mm·s−1, the combustion is stable, and the maximum burning light intensity is 2.053 cd. [ABSTRACT FROM AUTHOR]

Injecting power plant flue gas into a goaf stores CO2 in the flue gas and effectively prevents the spontaneous combustion of the coal remaining in the goaf. Here, we investigated the adsorption behavior of three types of coal at normal temperature and pressure using a self-developed adsorption experimental device. We used a specific surface area and porosity analyzer to study the effects of pore structure, mineral content, and moisture content on CO2 adsorption in coal. Based on the experimental data, we designed a multifactor CO2 adsorption prediction model based on a backpropagation (BP) neural network. The results indicated that the pore size of most micropores in coal was in the range of 0.5–0.7 and 0.8–0.9 nm. The specific surface area and pore volume were positively correlated with the CO2-saturated adsorption capacity, whereas the mean pore diameter, mineral content, and moisture content were inversely associated with the CO2-saturated adsorption amount. The accuracy of the multifactor BP neural network prediction model was satisfactory: the determination coefficients (R2) of the training and test sets were both above 0.98, the root mean square error (RMSE) and mean absolute error (MAE) of the test set were both less than 0.1, and the prediction results satisfied the requirements. To optimize the prediction performance of the model, we used the random forest algorithm to calculate the importance of each factor. The sum of the importance weights of the specific surface area, moisture content, and pore volume was 91.6%, which was much higher than that of the other two factors. Therefore, we constructed an optimization model with specific surface area, moisture content, and pore volume as input variables. The R2 values of the training and test sets in the simplified model were improved compared with those of the multifactor model, the RMSE and MAE were reduced, and the fitting effect was ideal. The prediction model of CO2 adsorption in coal based on the BP neural network can predict the CO2 adsorption capacity of coal under different physical and chemical conditions, thereby providing theoretical support for the application of CO2 storage technology in goafs. [ABSTRACT FROM AUTHOR]

The paper introduced different testing methods and testing theories for broken rock zone of surrounding rock of roadway, discussed and analyzed advantages and disadvantages and scope of application of the different testing methods. By comparison, it got conclusion as follows: technology of the acoustic method is advanced and testing method is simple, but it is difficult to be applied in soft rock; the multi-point displacement measurement and the multi-point stress measurement can be applied for real-time monitoring of surrounding rock displacement or stress, but the accuracy of measurement is limited; the seismic wave method has high testing precision, but its equipment is expensive and complicated to install; the geologic radar method without drilling is a non-destructive testing method, but testing cost is high; the resistivity method has a high requirement in the precision of instrument and arrangement of electrodes; image resolution of the method of borehole camera is limited by harsh environmental conditions within the borehole and the pixel of camera. Therefore, each testing method has its limitations, nature of the surrounding rock should be considered in actual application to select appropriate method for accurate determination of broken rock zone of the roadway.