Underground space creation and energy extraction, which induce unloading on rock fractures, commonly occur in various rock engineering projects, and rock engineering projects are subjected to high temperatures with in...Underground space creation and energy extraction, which induce unloading on rock fractures, commonly occur in various rock engineering projects, and rock engineering projects are subjected to high temperatures with increasing depth. Fluid flow behavior of rock fractures is a critical issue in many subsurface rock engineering projects. Previous studies have extensively considered permeability evolution in rock fractures under loading phase, whereas changes in fracture permeability under unloading phase have not been fully understood. To examine the unloading-induced changes in fracture permeability under different temperatures, we performed water flow-through tests on fractured rock samples subjected to decreasing confining pressures and different temperatures. The experimental results show that the permeability of fracture increases with unloading of confining pressure but decreases with loading-unloading cycles. Temperature may affect fracture permeability when it is higher than a certain threshold. An empirical model of fracture hydraulic aperture including two material parameters of initial normal stiffness and maximum normal closure can well describe the permeability changes in rough rock fracture subjected to loading-unloading cycles and heating. A coupled thermo-mechanical model considering asperity damage is finally used to understand the influences of stress paths and temperatures on fracture permeability.展开更多
Predicting the stages of cancer accurately is crucial for effective treatment planning. In this study, we aimed to develop a model using gene expression data and XGBoost (eXtreme Gradient Boosting) that include clinic...Predicting the stages of cancer accurately is crucial for effective treatment planning. In this study, we aimed to develop a model using gene expression data and XGBoost (eXtreme Gradient Boosting) that include clinical and demographic variables to predict specific lung cancer stages in patients. By conducting the feature selection using the Wilcoxon Rank Test, we picked the most impactful genes associated with lung cancer stage prediction. Our model achieved an overall accuracy of 82% in classifying lung cancer stages according to patients’ gene expression data. These findings demonstrate the potential of gene expression analysis and machine learning techniques in improving the accuracy of lung cancer stage prediction, aiding in personalized treatment decisions.展开更多
基金supported by the National Natural Science Foun-dation of China(Grant Nos.U2067203 and 42277140)Tsinghua University Initiative Scientific Research Program(Grant No.2022Z11QYJ006).
文摘Underground space creation and energy extraction, which induce unloading on rock fractures, commonly occur in various rock engineering projects, and rock engineering projects are subjected to high temperatures with increasing depth. Fluid flow behavior of rock fractures is a critical issue in many subsurface rock engineering projects. Previous studies have extensively considered permeability evolution in rock fractures under loading phase, whereas changes in fracture permeability under unloading phase have not been fully understood. To examine the unloading-induced changes in fracture permeability under different temperatures, we performed water flow-through tests on fractured rock samples subjected to decreasing confining pressures and different temperatures. The experimental results show that the permeability of fracture increases with unloading of confining pressure but decreases with loading-unloading cycles. Temperature may affect fracture permeability when it is higher than a certain threshold. An empirical model of fracture hydraulic aperture including two material parameters of initial normal stiffness and maximum normal closure can well describe the permeability changes in rough rock fracture subjected to loading-unloading cycles and heating. A coupled thermo-mechanical model considering asperity damage is finally used to understand the influences of stress paths and temperatures on fracture permeability.
文摘Predicting the stages of cancer accurately is crucial for effective treatment planning. In this study, we aimed to develop a model using gene expression data and XGBoost (eXtreme Gradient Boosting) that include clinical and demographic variables to predict specific lung cancer stages in patients. By conducting the feature selection using the Wilcoxon Rank Test, we picked the most impactful genes associated with lung cancer stage prediction. Our model achieved an overall accuracy of 82% in classifying lung cancer stages according to patients’ gene expression data. These findings demonstrate the potential of gene expression analysis and machine learning techniques in improving the accuracy of lung cancer stage prediction, aiding in personalized treatment decisions.
