Abstract: A SPECT imaging prediction model creation method, apparatus, device, and storage medium. The method includes: obtaining a training set including a plurality of scanning image groups, wherein each scanning image group includes a standard acquisition duration SPECT image and a short acquisition duration SPECT image that corresponding to each other; performing network construction on the basis of deep convolutional neural network to obtain a network to be trained; taking the short acquisition duration SPECT image in the training set as input-side training data, taking the standard acquisition duration SPECT image in the training set as output-side training data, training the network to be trained to obtain a SPECT imaging prediction model, so as to predict SPECT prediction image of short acquisition duration SPECT image under standard acquisition duration by using the SPECT imaging prediction model. The SPECT imaging time is significantly reduced while maintaining the imaging quality of medical images.

Abstract: A wave energy capture system can include a plurality of arm assemblies. Each arm assembly includes a floatation device and an arm that is coupled to the floatation device and to a body via a pivot. The arm assemblies independently pivot around the pivots with respect to the body in response to movement of the floatation devices caused by waves in water. A mechanical energy capture system converts the independent pivoting of the plurality of arm assemblies around the pivots to electrical energy and provides the electrical energy to either an electronic device that is electrically coupled to the mechanical energy capture system to power the electronic device, or to a battery to recharge the battery.

Abstract: A wave energy capture system deployed in water converts mechanical motion induced by waves in the water to electrical energy. A controller of the wave energy capture system receives input regarding real-time wave conditions in a vicinity of the wave energy capture system. The controller applies a control model to the received input to select a value of a control parameter for the wave energy capture system, where the control model includes a model that has been trained using machine learning to take wave condition data as input and to output control parameter values selected based on the wave condition data in order to increase an amount of energy captured by the wave energy capture system. The controller implements the selected value of the control parameter on the wave energy capture system.