Numerical Simulation of the Thermal-Hydro-Mechanical Characteristics of High-Speed Railway Roadbeds in Seasonally Frozen Regions

A multiphysics mathematical model of high-speed railway (HSR) roadbeds is necessary to facilitate a good level of understanding of the frost heaving mechanism. Based on the classical hydrodynamic model and fundamental thermoelasticity theories, we propose a thermo-hydro coupled model, based on the soil-water characteristic curve and solid-liquid ratio as the relation equations, with the effects of the ice-water phase change and water migration due to temperature change considered. With the linear expansion coefficient related to the temperature and the mass of ice content in roadbeds as the relation equation, we establish a macroscopic thermal-hydro-mechanical model for unsaturated soil to calculate the roadbed deformations. Based upon the field data of a typical cross section of the Harbin-Dalian HSR roadbed, the variation of the thermal-hydro-mechanical characteristics is simulated and studied. The results demonstrate that the increase of water content in the roadbed’s central line mainly appears in soil layers at depths less than 1.2 m and most ice-containing soil layers are at depths less than 0.6 m. Under the driving force of thermal and hydraulic migration, the vertical displacement of the west shoulder is increased to 18 mm. Then the settled maximum surface unevenness reaches 16 mm between the shoulder and centre line

Impact of preoperative white blood cell count on outcomes in different stage colorectal cancer patients undergoing surgical resection: a single-institution retrospective cohort study

Abstract Purpose To explore the association between preoperative WBC count and the long-term survival outcomes and clinical outcomes in different stage patients who underwent surgical resection for colorectal cancer (CRC). Patients and methods A cohort of 8121 Chinese patients who underwent surgical resection for CRC from January 1, 2008 to December 31, 2014 were enrolled as part of the retrospective cohort were retrospectively analyzed. Based on that the preoperative WBC optimal cut-off value was 7*109/L (7,000/µL), the high preoperative WBC group and the low preoperative WBC group was defined. Inverse probability of treatment weighting (IPTW) using the propensity score was used to reduce confounding. The impact of preoperative WBC count on overall survival (OS) and disease-free survival (DFS) was investigated using the Kaplan-Meier method and Univariate Cox proportional hazards models in different stage subgroup respectively. Results After IPTW, the clinical characters in the high preoperative WBC count group and the low preoperative WBC count group were balanced. Kaplan-Meier analysis showed that the 5-year OS rate were significantly lower in the high preoperative WBC count group overall, in stage II and IV. The 5-year DFS rate was significantly lower overall, in stage II and III in the high preoperative WBC count group. High preoperative WBC count was associated with poorer OS overall in stage II and stage IV. Conclusions This study suggests that preoperative WBC count is an independent risk factor for survival in patients undergoing colorectal surgery and may need to consider the stage of cancer when applied to predict long-term adverse outcome prognosis

ACPA-Net: Atrous Channel Pyramid Attention Network for Segmentation of Leakage in Rail Tunnel Linings

The automatic segmentation of leakage in rail tunnel linings is a useful and challenging task. Unlike other scenarios, the complex environment inside the tunnels makes it difficult to obtain accurate results for the segmentation of leakages. Some deep learning-based methods have been used to automatically segment tunnel leakage, but these methods ignore the interdependencies between feature channels, which are very important for extracting robust leakage feature representations. In this work, we propose an atrous channel pyramid attention network (ACPA-Net) for rail tunnel lining leakage segmentation. In ACPA-Net, the proposed atrous channel pyramid attention (ACPA) module is added into a U-shaped segmentation network. The ACPA module can effectively strengthen the representation ability of ACPA-Net by explicitly modeling the dependencies between feature channels. In addition, a deep supervision strategy that helps ACPA-Net improve its discrimination ability has also been introduced into ACPA-Net. A rail tunnel leakage image dataset consisting of 1370 images with manual annotation maps is built to verify the effectiveness of ACPA-Net. The final experiment shows that ACPA-Net achieves state-of-the-art performance on the Crack500 dataset and our rail tunnel leakage image dataset, and our method has the least number of parameters of all the methods