Abstract: A charged depth filter for removing cells and/or cellular debris from a biopharma feedstock having a first functionalized nonwoven layer having a first calculated pore size and a first dynamic charge capacity; a second functionalized nonwoven layer having a second calculated pore size and a second dynamic charge capacity positioned after the first functionalized nonwoven layer in the direction of the biopharma feedstock flow, and wherein the first calculated pore size is greater than the second calculated pore size, and the first dynamic charge capacity is less than the second dynamic charge capacity.

Abstract: An example computing device includes: a wireless communications interface configured to connect to a network deployed by one or more access points; a controller for the wireless communications interface, the controller configured to: predict whether a target access point for connecting to the network has an active session with the computing device when the computing device is disconnected from the target access point; when the uncleared session is predicted, send a clearing request to the target access point to clear the active session; and connect to the target access point.

Abstract: Example tiered storage systems, storage devices, and methods provide intelligent data access across tiered storage systems. An example system can comprise one or more computing devices, a file system, an object storage system comprising an object storage, and a data tiering application. The data tiering application is executable by one or more computing devices to perform operations comprising determining, using machine learning logic, a cluster of associated files stored in the file system; and archiving the cluster of associated files from the file system to the object storage coupled for electronic communication to the file system via a computer network.

Abstract: Example tiered storage systems, storage devices, and methods provide intelligent data access across tiered storage systems. An example system can comprise one or more computing devices, a file system, an object storage system comprising an object storage, and a data tiering application. The data tiering application is executable by one or more computing devices to perform operations comprising determining, using machine learning logic, a cluster of associated files stored in the file system; and archiving the cluster of associated files from the file system to the object storage coupled for electronic communication to the file system via a computer network.

Abstract: A system and method for archiving and analyzing data are disclosed. The system receives event data associated with a process; responsive to receiving the event data, determines process data associated with the process; generates process metadata from the event data and the process data; and stores the event data, the process data, and the process metadata in a data repository organized by the process metadata. Since the process data is determined early on in the data pipeline, the system can significantly reduce the amount of computation required for generating data analytics. The system is also capable of providing analytic results computed against a massive amount of archived data in real-time or near real-time as user requests are initiated. Efficiency of process mining and process optimization is also improved due to enhanced information stored for archived processes.

Abstract: A system and method for archiving and analyzing data are disclosed. The system receives event data associated with a process; responsive to receiving the event data, determines process data associated with the process; generates process metadata from the event data and the process data; and stores the event data, the process data, and the process metadata in a data repository organized by the process metadata. Since the process data is determined early on in the data pipeline, the system can significantly reduce the amount of computation required for generating data analytics. The system is also capable of providing analytic results computed against a massive amount of archived data in real-time or near real-time as user requests are initiated. Efficiency of process mining and process optimization is also improved due to enhanced information stored for archived processes.