Abstract: A method of trading electrical energy is provided. The method comprises a smart agent receiving state data affecting electricity usage within an electrical power grid over a specified time period and forecasting, with a supply/demand model, supply and demand for electricity within the power grid according to the state data. The smart agent uses a reinforced learning neural network to calculate a price for electricity according the state data and forecasted supply and demand. The smart agent submits an order to a matching engine to buy or sell electricity on the power grid at the calculated price according to specified market rules. The smart engine receives an acknowledgment from the matching engine if the order is matched to another agent on the power grid or a rejection from the matching engine if the order is not matched to another agent.

Abstract: The disclosure provides devices for easy and quick isolation of target analytes in a point-of-care setting. In some aspects, the devices comprise a first compartment, a second compartment, and an adaptor. The adaptor comprises a removable/positionable magnet and, in a first position of the adaptor, the magnet immobilizes magnetic particles within the first compartment and in a second position of the adaptor, the magnet immobilizes magnetic particles within the second compartment. Moving the adaptor from the first position into the second position causes a sealed gas-phase transfer of magnetic particles from the first compartment to the second compartment. The magnetic particles bind to target analytes and, thus, moving the magnetic particles from the first compartment into the second compartment isolates the target analytes. The target analytes can be retrieved from the second compartment and analyzed. Methods of isolating target analytes using the devices disclosed herein are also provided.

Abstract: A medical manipulator includes a manipulator flexible portion extending in a longitudinal direction; and two arms arranged at a distal end of the manipulator flexible portion, and each of the two arms includes a first bending portion, the first bending portion has an artificial muscle that drives the first bending portion to bend, and a bending tube, and the artificial muscle and the bending tube extend along the longitudinal direction and are arranged side by side.

Abstract: A medical manipulator includes an outer sheath, a channel tube that passes through the outer sheath, a bendable bending portion disposed at the distal end of the outer sheath, and a bending wire that bends the bending portion, and the bending portion includes a plurality of ring members through which the bending wire passes, and the ring members are spaced apart along the longitudinal direction of the outer sheath.

Abstract: A medical manipulator includes: a manipulator; and a camera unit provided at a distal end of the manipulator so as to be movable back and forth in a longitudinal direction, and the camera unit includes a support portion, and a camera provided at a distal end side of the support portion, and the camera unit includes a first state in which the camera unit advances relative to the manipulator, a second state in which the camera unit retracts relative to the manipulator, and a third state in which the camera unit bends downward.

Abstract: A medical manipulator system includes: an insertion manipulator extending in a longitudinal direction and having a lumen; a treatment manipulator capable of passing through the lumen; a first drive unit that drives the insertion manipulator, and a second drive unit that drives the treatment manipulator, and the first drive unit is disposed at a distal end side of the second drive unit and is provided so as to be movable forward and backward in the longitudinal direction.

Abstract: A medical manipulator includes a manipulator flexible portion extending in a longitudinal direction; and two arms arranged at a distal end of the manipulator flexible portion, and the two arms are arranged in a left-right direction perpendicular to the longitudinal direction, each of the two arms includes a second bending portion, and distal ends of second bending portions of the two arms are spaced apart in the left-right direction by bending the second bending portions.

Abstract: A method of trading electrical energy is provided. The method comprises a smart agent receiving state data affecting electricity usage within an electrical power grid over a specified time period and forecasting, with a supply/demand model, supply and demand for electricity within the power grid according to the state data. The smart agent uses a reinforced learning neural network to calculate a price for electricity according the state data and forecasted supply and demand. The smart agent submits an order to a matching engine to buy or sell electricity on the power grid at the calculated price according to specified market rules. The smart engine receives an acknowledgment from the matching engine if the order is matched to another agent on the power grid or a rejection from the matching engine if the order is not matched to another agent.

Abstract: A method of trading electrical energy is provided. The method comprises a smart agent receiving state data affecting electricity usage within an electrical power grid over a specified time period and forecasting, with a supply/demand model, supply and demand for electricity within the power grid according to the state data. The smart agent uses a reinforced learning neural network to calculate a price for electricity according the state data and forecasted supply and demand. The smart agent submits an order to a matching engine to buy or sell electricity on the power grid at the calculated price according to specified market rules. The smart engine receives an acknowledgment from the matching engine if the order is matched to another agent on the power grid or a rejection from the matching engine if the order is not matched to another agent.

Abstract: A method of trading electrical energy is provided. The method comprises a smart agent receiving state data affecting electricity usage within an electrical power grid over a specified time period and forecasting, with a supply/demand model, supply and demand for electricity within the power grid according to the state data. The smart agent uses a reinforced learning neural network to calculate a price for electricity according the state data and forecasted supply and demand. The smart agent submits an order to a matching engine to buy or sell electricity on the power grid at the calculated price according to specified market rules. The smart engine receives an acknowledgment from the matching engine if the order is matched to another agent on the power grid or a rejection from the matching engine if the order is not matched to another agent.