Flash Flood Simulation Using Geomorphic Unit Hydrograph Method: Case Study Of Headwater Catchment Of Xiapu River Basin, China

The flash flood refers to flood produced by heavy local rainfalls and often occurs in mountainous areas. It is characterized by a quick rise of water level causing a great threat to the lives of those exposed. Many countries and regions face the threat of flash floods. However, some traditional hydrological models can hardly simulate the flash flood process well due to the lack of hydrological data and the insufficient understanding of complicated runoff mechanism in mountainous and hilly areas. According to this condition, a new hydrological model based on the framework of Xinanjiang model, widely used in humid and semi-humid regions in China, is presented to simulate flash flood. The highlight of new model is using the geomorphic unit hydrograph (GUH) method to simulate the overland flow process. This method has clear physical concept and can easily provide unit hydrographs of various time intervals only based on DEM data. This feature makes the method extremely valuable in ungauged catchment. The new presented hydrological model is used in the headwater catchment of Xiapu River basin and the results demonstrate that the computed data generally agrees well with the measured data and it can be treated as a useful tool for flash flood hazard assessment in mountainous catchment

Analysis on Safety of Removing the Closure Segment in a Prestressed Concrete Cable-stayed Bridge

AbstractAiming at failure of closure segment in a prestressed concrete cable-stayed bridge, a strengthening technology, namely replacing the closure segment, was firstly put forward. But removing the old closure segment was a process of release of internal force and had great risk. So the structural safety possibly induced by removing must be analyzed and confirmed. Based on FEM and summary of engineering experience, the construction stages for removing the old closure segment were simulated, and then some analysis relevant to safety, including thermal effect, dynamic characteristics and global stability of the whole bridge structure, were systematically presented. According to these analysis results, corresponding prevention and control measures were provided to ensure construction safety. Studies showed that, variation range of its structural state between before and after removing is not obvious, and its dynamic characteristics changed little after removing. In addition, structural instability could not be induced by removing, but for the sake of improving construction safety reliability, necessary safety prevention and control measures were indispensable. Analysis on safety of removing the old closure segment constituted the important part of the strengthening technology of replacing the closure segment, and became the theoretical basis of removing partial structural members for existing bridges

An auxiliary ordinary differential equation and the exp-function method

AbstractIn this paper, the new idea of finding the exact solutions of the nonlinear evolution equations is introduced. The idea is that the exact solutions of the auxiliary ordinary differential equation are derived by using exp-function method, and then the exact solutions of the nonlinear evolution equations are derived with aid of the auxiliary ordinary differential equation. As examples, the classical KdV equation, Boussinesq equation, (3+1)-dimensional Jimbo–Miwa equation and Benjamin–Bona–Mahony equation are discussed and the exact solutions are derived

A representation learning model based on variational inference and graph autoencoder for predicting lncRNA‑disease associations

Background: Numerous studies have demonstrated that long non-coding RNAs are related to plenty of human diseases. Therefore, it is crucial to predict potential lncRNAdisease associations for disease prognosis, diagnosis and therapy. Dozens of machine learning and deep learning algorithms have been adopted to this problem, yet it is still challenging to learn efficient low-dimensional representations from high-dimensional features of lncRNAs and diseases to predict unknown lncRNA-disease associations accurately. Results: We proposed an end-to-end model, VGAELDA, which integrates variational inference and graph autoencoders for lncRNA-disease associations prediction. VGAELDA contains two kinds of graph autoencoders. Variational graph autoencoders (VGAE) infer representations from features of lncRNAs and diseases respectively, while graph autoencoders propagate labels via known lncRNA-disease associations. These two kinds of autoencoders are trained alternately by adopting variational expectation maximization algorithm. The integration of both the VGAE for graph representation learning, and the alternate training via variational inference, strengthens the capability of VGAELDA to capture efficient low-dimensional representations from high-dimensional features, and hence promotes the robustness and preciseness for predicting unknown lncRNA-disease associations. Further analysis illuminates that the designed co-training framework of lncRNA and disease for VGAELDA solves a geometric matrix completion problem for capturing efficient low-dimensional representations via a deep learning approach. Conclusion: Cross validations and numerical experiments illustrate that VGAELDA outperforms the current state-of-the-art methods in lncRNA-disease association prediction. Case studies indicate that VGAELDA is capable of detecting potential lncRNAdisease associations. The source code and data are available at https:// github. com/ zhang labNKU/ VGAEL DA