Abstract: A computer-implemented method for predicting non-communicable diseases with infectious risk factors using artificial intelligence includes detecting one or more risk factors associated with a non-communicable disease based on a graph associated with person-to-person links, generating a data structure for compactly representing the graph to compute at least one person-to-person distance, and performing a machine learning technique with regularization of the at least one person-to-person distance.

Abstract: A computer-implemented method for predicting non-communicable diseases with infectious risk factors using artificial intelligence includes detecting one or more risk factors associated with a non-communicable disease based on a graph associated with person-to-person links, generating a data structure for compactly representing the graph to compute at least one person-to-person distance, and performing a machine learning technique with regularization of the at least one person-to-person distance.

Abstract: A method includes collecting an arrival time preference from suppliers of a hub-and-spoke network; determining an equilibrium arrival time solution for each of the suppliers; determining, using the equilibrium arrival time solution for each of the suppliers, a set of allocated arrival times at a hub for each of the suppliers; collecting an updated arrival time preference from each of the suppliers; determining, using the updated arrival time preference from each of the suppliers, a subsequent equilibrium arrival time solution for the suppliers; determining, using the subsequent equilibrium arrival time solution for each of the suppliers, a subsequent set of allocated arrival times at the hub for each of the suppliers; scheduling, based on the subsequent set of allocated arrival times, an arrival time solution at the hub for each of the suppliers; and outputting, at the hub, the scheduled arrival time solution for each of the suppliers.