Abstract: A method of forming a glass sheet comprises: (a) forming a ribbon of glass from molten glass with a pair of forming rollers; (b) reducing horizontal temperature variability of the ribbon of glass to be 10° C. or less across 80 percent of an entire width of the ribbon of glass before the ribbon of glass cools to a glass transition temperature; (c) controlling a cooling rate of the ribbon of glass while the ribbon of glass moves vertically downward within a setting zone such that the ribbon of glass has a first average cooling rate before the ribbon of glass cools to the glass transition temperature and a second average cooling rate after the ribbon of glass cools to the glass transition temperature, the first average cooling rate being less than the second average cooling rate; and (d) separating a glass sheet from the ribbon of glass.

Abstract: A method and system for assessing the risk of a cardiac event in a patient which utilizes real-time and historical data from Electronic Medical Record (EMR) systems is described. A risk of a cardiac event is estimated, in real-time or near-real-time, for a patient who is currently in a hospital emergency department. Batch data for one or more past patients is extracted from EMRs into a machine learning model. Using the machine learning model, a risk level for one or more past patients is calculated. A real-time database is constructed from streams of real-time Health Level 7 (HL7) clinical data, wherein at least one stream of real-time HL7 data is associated with the current patient, and a risk prediction is estimated by joining the calculated risk level for the patient in the machine learning model with the real-time HL7 clinical data from the patient.

Abstract: A method and system for assessing the risk of a cardiac event in a patient which utilizes real-time and historical data from Electronic Medical Record (EMR) systems is described. A risk of a cardiac event is estimated, in real-time or near-real-time, for a patient who is currently in a hospital emergency department. Batch data for one or more past patients is extracted from EMRs into a machine learning model. Using the machine learning model, a risk level for one or more past patients is calculated. A real-time database is constructed from streams of real-time Health Level 7 (HL7) clinical data, wherein at least one stream of real-time HL7 clinical data is associated with the current patient, and a risk prediction is estimated by joining the calculated risk level for the patient in the machine learning model with the real-time HL7 clinical data from the patient.