Abstract: A medical rehabilitation bed is disclosed, relating to the technical field of self-help rehabilitation bed structures in medical instrument products. The medical rehabilitation bed comprises a bed head, a bed tail and a bed board. A pull rod is arranged at the bottom of the bed board; one end of the pull rod is mechanically connected to a bed head gearbox arranged at the bed head, and the other end of the pull rod is mechanically connected to a bed tail gear device arranged at the bed tail. Transmission systems having the same structure and capable of controlling the bed head and the bed tail to ascend and descend are arranged in the bed head and the bed tail, respectively. The transmission systems are mechanically connected to the bed head gearbox and the bed tail gear device, respectively. The medical rehabilitation bed has a compact structure and comprehensive functions.

Abstract: A medical rehabilitation bed is disclosed, relating to the technical field of self-help rehabilitation bed structures in medical instrument products. The medical rehabilitation bed comprises a bed head, a bed tail and a bed board. A pull rod is arranged at the bottom of the bed board; one end of the pull rod is mechanically connected to a bed head gearbox arranged at the bed head, and the other end of the pull rod is mechanically connected to a bed tail gear device arranged at the bed tail. Transmission systems having the same structure and capable of controlling the bed head and the bed tail to ascend and descend are arranged in the bed head and the bed tail, respectively. The transmission systems are mechanically connected to the bed head gearbox and the bed tail gear device, respectively. The medical rehabilitation bed has a compact structure and comprehensive functions.

Abstract: An intrusion detection method, system and computer-readable media are disclosed. The system can include a processor programmed to perform computer network intrusion detection. The intrusion detection can include an identification module and a detection module. The identification module can be adapted to perform semi-supervised machine learning to identify key components of a network attack and develop MDL models representing those attack components. The detection module can cluster the MDL models and use the clustered MDL models to classify network activity and detect polymorphic or zero-day attacks.