Titlebook: Clinical Examination in Orthopedics; Mohan Iyer Book 2012 Springer-Verlag London Limited 2012

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書目名稱Clinical Examination in Orthopedics影響因子(影響力)




書目名稱Clinical Examination in Orthopedics影響因子(影響力)學科排名




書目名稱Clinical Examination in Orthopedics網絡公開度




書目名稱Clinical Examination in Orthopedics網絡公開度學科排名




書目名稱Clinical Examination in Orthopedics被引頻次




書目名稱Clinical Examination in Orthopedics被引頻次學科排名




書目名稱Clinical Examination in Orthopedics年度引用




書目名稱Clinical Examination in Orthopedics年度引用學科排名




書目名稱Clinical Examination in Orthopedics讀者反饋




書目名稱Clinical Examination in Orthopedics讀者反饋學科排名

Interim

Mohan IyerProvides examples and step-by-step instructions.Comprehensive in scope.Provides a clear overview of the basics.Includes supplementary material:

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Cryptic

Book 2012ected part. By a thorough clinical examination, it can help an individual to arrive at a tentative differential diagnosis of the condition and hence help in investigating the same by numerous tests which are relevant to the given condition. The manner in which a given case is handled in this way is

morale

is far more important than the diagnosis of the condition and this feature comes with practicing the same many times. It is a must for anyone embarking in life on Orthopedics as a career.978-1-4471-5928-5978-0-85729-971-0

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Consensus Control in Networked Flexible-Joint Manipulator Systems via Energy Shaping,dynamics by using energy-shaping methodology plus damping injection. A novel decentralized controller is proposed to solve the leaderless consensus problems with undirected network topology. The total energy of the systems and the controllers, as a suitable Lyapunov function, are provided to ensure

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劇本

,CILF: Causality Inspired Learning Framework for?Out-of-Distribution Vehicle Trajectory Prediction,ting domain-variant causal and non-causal feature by encouraging causal sufficiency. We evaluate the performance of CILF in different vehicle trajectory prediction models on the mainstream datasets NGSIM and INTERACTION. Experiments show promising improvements in CILF on domain generalization.