Search

The southwest mountainous area of China is one of the areas with the most landslides in the world. In this paper, we used Ya'an City and Garzê Tibetan Autonomous Prefecture in Sichuan Province as the research areas to explore the identification application effects of large-area potential landslides using synthetic aperture radar (SAR) data with different wavelength types (Sentinel-1, ALOS-2), different processing methods (SBAS-InSAR, Stacking-InSAR), and different geological environmental conditions. The results show the following: (1) The effect of identifying landslides with different slope directions is largely affected by the satellite orbit direction; when we identify landslide hazards across a large area, the joint monitoring mode of ascending and descending orbit data is required. (2) The period of monitoring affects the identification effect of potential landslides when landslide identification is carried out in southwestern China; the InSAR monitoring period is recommended to be more than 2 years. (3) In different geological environmental regions, SBAS technology and Stacking technology have their own advantages; Stacking technology identifies more potential landslides, and SBAS technology identifies potential landslides with higher accuracy; (4) the degree of vegetation coverage has a great impact on the landslide identification effect of different SAR data sources. In low-density vegetation coverage areas, the landslide identification result using Sentinel-1 data seems to be better than the result using ALOS-2 data. In high-density vegetation coverage areas, the landslide identification result using ALOS-2 data is better than that using Sentinel-1 data. [ABSTRACT FROM AUTHOR]

Purpose: Risk factors and prognostic model of fatal outcomes need to be investigated for the increasing number of immunocompromised hosts (ICHs) who are hospitalized for severe pneumonia with high hospital mortality. Patients and Methods: In this single-center, retrospective study, we recruited 1,933 ICHs with severe pneumonia who were admitted to the intensive care unit (ICU) in West China hospital, Sichuan university, China between January, 2012 and December, 2018. Clinical features, laboratory findings, and fatal outcomes were collected from electronic medical records. Patients were randomly separated into a 70% training set (n=1,353) and a 30% testing set (n=580) for the development and validation of a prediction model. All data within 24 hours of ICU admission were compared between survivors and non-survivors. The risk factors were identified through LASSO and multivariate logistic regression analysis, and then used to develop a predicting nomogram. The nomogram for predicting hospital mortality of ICHs with severe pneumonia in the ICU was validated by C-index, AUC (area under the curve), calibration curve, and Decision Curve Analysis (DCA). Results: Eight risk factors, including age, fever, dyspnea, chronic renal disease, platelet counts, neutrophil counts, PaO2/FiO2 ratio, and the requirement for vasopressors, were adopted in a nomogram for predicting hospital mortality. The nomogram had great predicting accuracy with a C-index of 0.819 (95% CI=0.795– 0.842) in the training set, and a C-index of 0.819 (95% CI=0.783– 0.855) in the testing set for hospital mortality. Additionally, the nomogram had well-fitted calibration curves. DCA demonstrated that the nomogram was clinically beneficial. Conclusion: This study developed a novel nomogram for predicting hospital mortality of ICHs with severe pneumonia in the ICU. Validation showed good discriminatory ability and calibration, indicating that the nomogram was expected to be a superior predictive tool for doctors to identify risk factors and predict mortality, and might be generally applied in clinical practice after more external validations. [ABSTRACT FROM AUTHOR]

The urgent need to address climate change, driven by the increasing concentrations of greenhouse gases, has made it imperative for the world to focus on enhancing carbon emission mitigation and carbon utilization. Consequently, carbon-negative technologies have garnered significant attention, such as Carbon Capture, Utilization, and Storage (CCUS). In order to achieve the circular utilization of carbon and improve the economic viability of conventional carbon-negative technologies, this paper proposes the innovative concept of Carbon Capture, Circular Utilization, and Sequestration (CCCUS). This multifunctional carbon-negative technology couples underground biomethanation of carbon dioxide and hydrogen-rich industrial by-product gases with geothermal energy production. CCCUS also offers several additional functions, such as renewable natural gas storage, natural gas displacement, and carbon dioxide geological sequestration. Furthermore, a preliminary assessment of the techno-economic feasibility of CCCUS is undertaken. A numerical case study, conducted using the XGS gas storage in the Sichuan Basin, China, unveiled that over a 425-day period, the conversion ratio of carbon dioxide to methane in the working gas can achieve 98.9%. Moreover, the net present value (NPV) of CCCUS over a 30-year period is estimated to be approximately 4.67 × 108 CNY under the base scenario. These findings, along with field monitoring and laboratory experiments, preliminarily present evidence supporting the potential techno-economic feasibility of CCCUS. This paper offers a fresh perspective on the development of the circular carbon economy. [ABSTRACT FROM AUTHOR]