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Numerical modeling of sediment flushing from Lewis and Clark Lake

international Journal of Sediment Research 2013年第2期28卷 182-193页

作者： Jungkyu AHN Chih Ted YANG Paul M.BOYD Daniel B.PRiDAL john i.remus Civil and Environmental Engineering Department Seoul National UniversitySeoulRepublic of Korea Research Fellow Water Resources and Director of Hydroscience and Training Center Civil and Environmental Engineering DepartmentColorado State UniversityFort CollinsCOUSA80526 Borland Professor U.S.Army Corps of Engineers OmahaNebraskaUSA Hydraulic Engineer Sedimentation and Channel Stabilization Section U.S.Army Corps of EngineersOmahaNebraskaUSA Chief

Lewis and Clark Lake is located on the main stream of the Missouri *** reservoir is formed behind Gavins Point dam near Yankton,South Dakota,*** Lewis and Clark Lake reach extends about 40 km from the Gavins Point *** reservoir delta has been growing since the closure of Gavins Point dam in 1955 and has resulted in a 21%reduction of storage within the maximum pool of the *** several sediment management methods,drawdown flushing has been recommended as a possible management *** engineering viability of removing sediments deposited in the lake should be examined by numerical modeling before implementing a drawdown ***4 was used for this study and calibrated by using measured data from 1975 to *** cross-section changes and amount of flushed sediment were predicted with four hypothetical flow *** flushing efficiencies of all scenarios were estimated by comparing the ratios between water consumption and flushed sediment during flushing.

关键词： Drawdown flushing Flushing efficiency GSTARS computer model Reservoir sedimentation Sediment transport Lewis and Clark Lake

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Deep learning applied to the imaging diagnosis of hepatocellular carcinoma

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Artificial intelligence in Gastrointestinal Endoscopy 2021年第4期2卷 127-135页

作者： Vinícius remus Ballotin Lucas Goldmann Bigarella john Soldera Jonathan Soldera School of Medicine Universidade de Caxias do SulCaxias do Sul 95070-560RSBrazil Computer Science Federal Institute of EducationScience and Technology FarroupilhaSantoÂngelo 98806-700RSBrazil Clinical Gastroenterology Universidade de Caxias do SulCaxias do Sul 95070-560RSBrazil

Each year,hepatocellular carcinoma is diagnosed in more than half a million people *** is the fifth most common cancer in men and the seventh most common cancer in *** diagnosis is currently made using imaging techniques,such as computed tomography and magnetic resonance *** most cirrhotic patients,these methods are enough for diagnosis,foregoing the necessity of a liver *** order to improve outcomes and bypass obstacles,many companies and clinical centers have been trying to develop deep learning systems that could be able to diagnose and classify liver nodules in the cirrhotic liver,in which the neural networks are one of the most efficient approaches to accurately diagnose liver *** the advances in deep learning systems for the diagnosis of imaging techniques,there are many issues that need better development in order to make such technologies more useful in daily practice.

关键词： Hepatocellular carcinoma Cirrhosis Machine learning Artificial intelligence

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