[Advances in the research of application of artificial intelligence in burn field]

Artificial intelligence has been able to automatically learn and judge large-scale data to some extent. Based on database of a large amount of burn data and in-depth learning, artificial intelligence can assist burn surgeons to evaluate burn surface, diagnose burn depth, guide fluid supply during shock stage, and predict prognosis, with high accuracy. With the development of technology, artificial intelligence can provide more accurate information for burn surgeons to make clinical diagnosis and treatment strategies.

Using hydrological simulation to detect human-disturbed epoch in runoff series

Runoff in major rivers in China has been decreasing in recent decades, mainly due to climate change and human activity. River basin managers have a critical interest in detecting and diagnosing non-stationaries in runoff time series. Here we use a rainfall runoff model-based approach to identify the human-disturbed periods of the record. The method is applied to the Kuye River catchment, located in the Loess Plateau, China. The SimHyd model performs well for simulation of monthly natural discharges, and the method suggests that discernable human influence began in 1980. Anthropogenic effects were detectable several years earlier at the downstream stations than the upstream stations, consistent with pace and timing of soil and water conservation measures implemented across the Kuye River catchment.