Abstract: Machine-learning-based determination of event stability is provided to facilitate loose coupling of event coordination systems. The method includes training a machine learning model to determine stability of events of an event coordination system based on a plurality of attributes of the events, and using the machine learning model to determine an event stability for an event of the event coordination system. Further, the method includes comparing the determined event stability for the event to a stability threshold, and determining that the event is a stable event based on the event stability extending the stability threshold. Based on determining that the event is the stable event, the stable event is propagated from the event coordination system to another event coordination system.

Abstract: A method includes: determining, by a computing device, an operating condition of a machine in an industrial environment; determining, by the computing device, a hazard based on the operating condition; determining, by the computing device, one or more safety precautions based on the hazard; generating, by the computing device, a virtual three-dimensional holographic safety gate around the machine based on the one or more safety precautions.

Abstract: A method includes: determining, by a computing device, an operating condition of a machine in an industrial environment; determining, by the computing device, a hazard based on the operating condition; determining, by the computing device, one or more safety precautions based on the hazard; generating, by the computing device, a virtual three-dimensional holographic safety gate around the machine based on the one or more safety precautions.