Abstract: Embodiments are disclosed for determining delivery confidence intervals. An example method for determining a confidence interval includes the following operations. Delivery information is received from one or more sources, wherein the delivery information comprises data associated with at least one predefined location perimeter. The data associated with the at least one predefined location perimeter is normalized. The normalized data is categorized into training data used to perform a deep neural network regression analysis. A predicted delivery confidence interval is determined by constructing a predictive learning model by conducting a regression of the data using deep neural network regression. The predicted delivery confidence interval is stored in a results table in association with the predefined location perimeter. And, upon receiving a request from a visibility management system, accessing the results table to provide predicted delivery windows to consignees.

Abstract: Embodiments are disclosed for autonomously predicting shipper behavior. An example method includes the following operations. One or more learning models are generated. Shipper behavior data for at least one shipper is extracted. The shipper behavior data includes a plurality of features associated with the at least one shipper scheduled to ship one or more parcels. It is predicted whether one or more shipments will be sent or arrive at a particular time based at least in part on running the plurality of features of the at least one shipper through the one or more learning models.

Abstract: Embodiments are disclosed for autonomously predicting shipper behavior. An example method includes the following operations. One or more learning models are generated. Shipper behavior data for at least one shipper is extracted. The shipper behavior data includes a plurality of features associated with the at least one shipper scheduled to ship one or more parcels. It is predicted whether one or more shipments will be sent or arrive at a particular time based at least in part on running the plurality of features of the at least one shipper through the one or more learning models.

Abstract: There is disclosed a simulation system for simulating a respiratory system. The simulation system includes a variable resistance device that provides a variable resistance to the airflow it receives to simulate a variation in resistance for the respiratory system during breathing and a variable elastance device that provides a variable elastance to the airflow it receives to simulate a variation in elastance for the respiratory system during breathing.

Abstract: Embodiments are disclosed for determining delivery confidence intervals. An example method for determining a confidence interval includes the following operations. Delivery information is received from one or more sources, wherein the delivery information comprises data associated with at least one predefined location perimeter. The data associated with the at least one predefined location perimeter is normalized. The normalized data is categorized into training data used to perform a deep neural network regression analysis. A predicted delivery confidence interval is determined by constructing a predictive learning model by conducting a regression of the data using deep neural network regression. The predicted delivery confidence interval is stored in a results table in association with the predefined location perimeter. And, upon receiving a request from a visibility management system, accessing the results table to provide predicted delivery windows to consignees.

Abstract: There is disclosed a simulation system for simulating a respiratory system. The simulation system includes a variable resistance device that provides a variable resistance to the airflow it receives to simulate a variation in resistance for the respiratory system during breathing and a variable elastance device that provides a variable elastance to the airflow it receives to simulate a variation in elastance for the respiratory system during breathing.

Abstract: Embodiments are disclosed for autonomously clustering shipping units. An example method includes accessing clustering information units from a clustering data management tool. The example method further includes extracting features from clustering information units, wherein the features are representative of one or more of the shipper behavior data and the package information. Exemplary shipping units are shippers, buildings handling packages, package delivery drivers, and package handlers. The example method further includes generating, using a shipping unit clustering learning model and the features, an output comprising cluster of shipping units. Corresponding apparatuses and non-transitory computer readable storage media are also provided.

Abstract: Embodiments are disclosed for autonomously predicting shipper behavior. An example method includes the following operations. One or more learning models are generated. Shipper behavior data for at least one shipper is extracted. The shipper behavior data includes a plurality of features associated with the at least one shipper scheduled to ship one or more parcels. It is predicted whether one or more shipments will be sent or arrive at a particular time based at least in part on running the plurality of features of the at least one shipper through the one or more learning models.

Abstract: Embodiments are disclosed for autonomously generating volume forecasts. An example method includes accessing volume information units from a volume forecast data management tool. The example method further includes extracting features from volume information units, wherein the features are representative of one or more of a package received time, or package information. The features can be categorized by different hierarchical level information. The example method further includes generating, using a volume forecast learning model and the features, an output comprising a volume forecast for a particular hierarchical level. Corresponding apparatuses and non-transitory computer readable storage media are also provided.

Abstract: Methods for determining indicators of left ventricular diastolic function are disclosed. The indicators may include the left ventricular isovolumetric relaxation time and the negative slope of a left atrial “V” wave.