Abstract: Embodiments of the present disclosure provide a method for identifying those entities within a network that have the most influence on other entities within the network. A multi-relational network comprising links among a plurality of physicians is generated based on peer network data, wherein each link indicates a first physician that influences a second physician, and a weight of the influence. A decision by a treating physician of the plurality of physicians is decomposed, using a deep learning engine, into a magnitude of peer influence and a magnitude of control factor influence based on the multi-relational network and a plurality of control factors respectively. The magnitude of peer influence among one or more physicians in the multi-relational network is distributed among physicians in the multi-relational network based on the links each physician maintains with other physicians.

Abstract: Cookware such as griddles and pots are formed with an oscillation mini-channel that winds back-and-forth between direct and indirect heating regions. An operating fluid that occupies 30-90 percent of the volume of the oscillation mini-channel is placed under vacuum. The mini-channel is dimensioned to produce capillary forces that create vapor bubbles and liquid slugs interspersed throughout the oscillation mini-channel. Heating of the direct heating region creates oscillatory movements of the vapor bubbles and liquid slugs that transfers heat from the direct heating region to the indirect heating region to maintain a more uniform temperature across the food-heating zone. The cookware may exhibit an effective thermal conductivity of at least 1,000 W/m·K.