Abstract: Jet pump clamps fit to modified jet pump assemblies at the riser pipe-restrainer bracket junction. The clamp can secure the restrainer bracket and riser pipe, relieving any welds between the same stress in the same, while preventing the restrainer bracket from moving. The clamp may include multiple members on either side of the restrainer bracket that fit into surfaces of the riser pipe. When these members are drawn together through clamping action, the underlying riser pipe is compressed. Similarly, vertically-adjustable members may seat into and/or through the restrainer bracket to hold the bracket steady. Jet pump assemblies may be prepared by forming grooves in the riser pipe and hole(s) in the restrainer bracket(s) and spherical indentations about the same. Clamps may then be installed on the grooves and through the hole(s) at installation or during a maintenance outage in a commercial nuclear power plant.

Abstract: An apparatus and method are provided for machine learning method using a network of agents. The agents are arranged in a network with respective links between pairs of agents, and the links enabling the exchange information. Different agents can apply different reasoning paradigms corresponding to different approaches to machine learning and artificial intelligence. These disparate approaches are seamlessly integrated to aggregate decisions and learning performed using different approaches using an economics model in which a Nash equilibrium is reached through the exchange of information. Each agent selects which other agents to exchange information with by seeking to optimize preference, utility, and objective functions, and these function include how well the agents obtain an assigned goal subject to other desirable features and characteristics (e.g., enforcing diversity).

Abstract: An apparatus and method are provided for machine learning method using a network of agents. The agents are arranged in a network with respective links between pairs of agents, and the links enabling the exchange information. Different agents can apply different reasoning paradigms corresponding to different approaches to machine learning and artificial intelligence. These disparate approaches are seamlessly integrated to aggregate decisions and learning performed using different approaches using an economics model in which a Nash equilibrium is reached through the exchange of information. Each agent selects which other agents to exchange information with by seeking to optimize preference, utility, and objective functions, and these function include how well the agents obtain an assigned goal subject to other desirable features and characteristics (e.g., enforcing diversity).