Sound absorption characteristics of loose bituminous coal porous media with different metamorphic degrees

Oxygen distribution and gaseous products change of coal fire based upon the semi-enclosed experimental system

Nonstick coal is widely distributed in the world and is an important resource for human beings. The explosion characteristics and explosion suppression of nonstick coal dust are increasingly concerning. In this paper, the flame and pressure characteristics of a nonstick coal dust explosion are studied, and the suppression effect of SiO2, KCl, and NH4H2PO4 on the explosion is analyzed. It is concluded that the maximum propagation distance of a nonstick coal dust explosion flame is 0.59 m, the maximum pressure is 0.63 MPa, and the maximum pressure rise rate is 40.79 MPa/s. The explosion suppression effect of SiO2 is worse than that of KCl, but the inhibition effect of NH4H2PO4 is the best among the three kinds of explosion suppressants. When the mass percentage of NH4H2PO4 dust mixed with coal dust is 70%, the coal dust explosion is completely suppressed. It absorbs a certain amount of heat to promote the chemical reaction, which plays a certain role in controlling the explosion process, and its explosion suppression mechanism includes both physical and chemical explosion suppression.

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Explosion Flame and Pressure Characteristics of Nonstick Coal Dust and the Inhibition of Explosion Suppressants

Inhibiting effects investigation of pulverized coal explosion using melamine cyanurate

Inhibiting effect investigation of ammonium dihydrogen phosphate on oxidative pyrolysis characteristics of bituminous coal

The inhibiting effects of sodium carbonate on coal dust deflagration based on thermal methods

This study explored the feasibility of radiofrequency (RF)-based radiomics analysis techniques for the preoperative prediction of programmed cell death protein 1 (PD-1) in patients with hepatocellular carcinoma (HCC).The RF-based radiomics analysis method used ultrasound multifeature maps calculated from the RF signals of HCC patients, including direct energy attenuation (DEA) feature map, skewness of spectrum difference (SSD) feature map, and noncentrality parameter S of the Rician distribution (NRD) feature map. From each of the above ultrasound maps, 345 high-throughput radiomics features were extracted. Then, the useful radiomics features were selected by the sparse representation method and input into support vector machine (SVM) classifier for PD-1 prediction.Among all the RF-based prediction models and the ultrasound grayscale comparative model, the RF-based model using all of the three ultrasound feature maps had the highest prediction accuracy (ACC) and area under the curve (AUC), which were 92.5% and 94.23%, respectively. The method proposed in this paper is effective for the meaningful feature extraction of RF signals and can effectively predict PD-1 in patients with HCC. 

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Prediction of programmed cell death protein 1 in hepatocellular carcinoma patients using radiomics analysis with radiofrequency-based ultrasound multifeature maps

/pdf/prediction-of-programmed-cell-death-protein-1-in-1660zdy0.pdf

Yang Xiao

Papers

The inhibiting effects of sodium carbonate on coal dust deflagration based on thermal methods

Inhibiting effects investigation of pulverized coal explosion using melamine cyanurate

Prediction of programmed cell death protein 1 in hepatocellular carcinoma patients using radiomics analysis with radiofrequency-based ultrasound multifeature maps

Prediction of programmed cell death protein 1 in hepatocellular carcinoma patients using radiomics analysis with radiofrequency-based ultrasound multifeature maps

Effects of Cf3i and Co2 on Methane-Air Explosion Pressure and Flame Propagation Characteristics