Abstract: A method for delivering a therapeutic agent to a subject from a transdermal delivery system is described, where the therapeutic agent (i) has a half-life in the blood when delivered orally of greater than about 48 hours and (ii) is for the treatment of a chronic condition. The transdermal delivery system achieves transdermal delivery of the therapeutic agent at steady state that is bioequivalent to administration of the therapeutic agent orally, wherein bioequivalency is established by (a) a 90% confidence interval of the relative mean Cmax and AUC of the therapeutic agent administered from the transdermal delivery system and via oral delivery between 0.70 and 1.43 or between 0.80 and 1.25, or (b) a 90% confidence interval of the ratios for AUC and Cmax of the therapeutic agent administered from the transdermal delivery system and via oral delivery between 0.70 and 1.43 or between 0.80 and 1.25.

Abstract: Techniques described herein relate to a method for managing jobs. Such techniques may include: generating a job completion entry that specifies that execution of a first job was completed within an estimated period of time for the first job, generating a second job completion entry that specifies that execution of a second job was not completed within a second estimated period of time for the second job, updating the job resource mapping model based on the first job completion entry and the second job completion entry to obtain a new updated job resource mapping model.

Abstract: A stator is described herein comprising a first longitudinally extending slot having a cavity defined by a first inwardly facing longitudinal surface and a second inwardly facing longitudinal surface; said stator further comprising a spacer provided in said cavity of said first slot, said spacer comprising a first spacer component that comprises a first sheet of material having a first sheet surface facing inwardly into said slot and an opposite sheet surface that is in contact with and extends along the first longitudinally extending inner surface of said slot and wherein said inwardly facing first sheet surface of said first spacer component comprises at least one ridge projection extending along its length L. Methods for assembling such stators are also described.

Abstract: An apparatus comprises a processing device configured to identify, at a first microservice, a service call that is to be transmitted to a second microservice, and to modify the service call to include context information, the context information characterizing a current state of execution of one or more tasks by one of the first microservice and the second microservice. The processing device is further configured to provide, from the first microservice to the second microservice, the modified service call including the context information. The context information enables re-initiation of said one of the first microservice and the second microservice to continue execution of the one or more tasks from the current state.

Abstract: A storage manager control application facilitates interactions with, and configurations of, a storage manager that manages one or more client computing devices of an information management system. The storage manager control application may be implemented using a variety of modules that allow a user to interact with the storage manager in various ways. Using the storage manager control application, a user may view a backup status of the information management system, and view the backup status of specific client computing devices that are managed by the storage manager. The user may also search for various entities and/or objects within the information management system using the storage manager control application. In addition, a user may view the status of one or more backup jobs configured within the information management system, as well as backup plans that have been established for one or more of the client computing devices.

Abstract: In one embodiment, at least one processing device is configured to assign a plurality of devices of a cluster to a logical host group where at least one of the devices assigned to the logical host group has a network time parameter that is different than another of the devices assigned to the logical host group. The at least one processing device is further configured to determine a target network time parameter for the logical host group based at least in part on network time parameter related information about a given device of the plurality of devices assigned to the logical host group and to cause the plurality of devices to configure their respective network time parameters to the target network time parameter based at least in part on the assignment of the plurality of devices to the logical host group.

Abstract: Methods, apparatus, and processor-readable storage media for device component management using deep learning techniques are provided herein. An example computer-implemented method includes obtaining telemetry data from one or more enterprise devices; determining, for each of the one or more enterprise devices, values for multiple device attributes by processing the obtained telemetry data; generating, for each of the one or more enterprise devices, at least one prediction related to lifecycle information of at least one device component by processing the determined attribute values using one or more deep learning techniques; and performing one or more automated actions based at least in part on the at least one generated prediction.

Abstract: In one embodiment, an exemplary method includes receiving multi-dimension information from a data domain operating system running on the server; determining that multiple drive failures occurred within a predetermined time frame based on the multi-dimension information; and extracting a list of system-level events and a timestamp of each event from the multi-dimension information. The method further includes determining a list of components impacted by the list of the system-level events based on the list of system-level events and the timestamp of each event; and determining one or more system-level events associated with one or more impacted components as root causes of the multiple drive failures based on the multi-dimension information. The method uses information from multiple regions of the DAE and correlate the information using a predetermined algorithm to automatically more efficiently identify one or more possible root causes of the multiple drive failures.

Abstract: A method comprises collecting data corresponding to operation of a user interface, analyzing the data and generating a dependency tree based at least in part on the analysis. The analyzing and the generating are performed using one or more machine learning techniques. The dependency tree comprises a plurality of nodes respectively corresponding to a plurality of components of the user interface and is organized at least in part according to one or more dependent relationships between the plurality of components. Based at least in part on a structure of the dependency tree, one or more test cases for the user interface are executed.

Abstract: Microstructure arrays and methods for using and manufacturing the arrays are described.

Abstract: A method and a drug delivery system for transdermally administering parathyroid hormone (PTH) in a pulsatile fashion are provided, where the drug delivery system comprises an array of microprojections each comprising PTH.

Abstract: Various systems and methods are provided for analyzing the effect(s) that a configuration change to one device has on other connected devices. In one embodiment, the disclosed functionality includes determining connectivity information associated with a data center, where the data center comprises at least a first device and a second device; discovering one or more changes to a configuration of the first device; determining, based at least in part on the connectivity information, that the second device is impacted by the one or more changes to the configuration of the first device; and determining one or more impacts to the second device as a result of the one or more changes, where each of the one or more impacts indicates a positive impact to the second device, a negative impact to the second device, or no impact to the second device.

Abstract: Methods and apparatus are described for making broadcast content available as an on-demand asset soon after all of the video fragments of the broadcast content have been made available. As the video fragments of the broadcast content are made available, they are requested and archived. When all of the fragments for the duration of the broadcast content are available (e.g., a live event ends), a VOD-style manifest is generated and the archived fragments are made available for downloading or streaming using the VOD-style manifest.

Abstract: A method includes extracting data pertaining to a plurality of user actions in connection with one or more changes to one or more of a plurality of applications, and training one or more machine learning models with the extracted data. The one more machine learning models are used to predict whether a user should receive a given notification in connection with a given change to a given application of the plurality of applications. In response to predicting that the user should receive the given notification, content of the given notification is determined. The method further includes generating the given notification for the user, and transmitting the given notification to the user.

Abstract: A component tracking system includes a plurality of devices that each include a device component monitoring subsystem that monitors for and records the addition and/or removal of device components to/from that device. A device component manager system that is coupled to each of the plurality of devices identifies, via a recording by a first device monitoring subsystem in a first device included in the plurality of devices, the removal of a first device component from the first device. The device component manager system then determines, via a recording by a second device component monitoring subsystem in a second device included in the plurality of devices, that the first device component has been added to the second device within a time period and, in response, the device component manager device prevents a removed first device component alarm.

Abstract: An apparatus comprises a processing device configured to identify, at a first microservice, a service call that is to be transmitted to a second microservice, and to modify the service call to include context information, the context information characterizing a current state of execution of one or more tasks by one of the first microservice and the second microservice. The processing device is further configured to provide, from the first microservice to the second microservice, the modified service call including the context information. The context information enables re-initiation of said one of the first microservice and the second microservice to continue execution of the one or more tasks from the current state.

Abstract: An adhesive matrix and adhesive formulation are described. The adhesive matrix is comprised of a hydrophilic domain and a hydrophobic domain, and a therapeutically active agent contained in the matrix in a supersaturated, stable, condition. The hydrophilic domain and the hydrophobic domain are co-soluble in a solvent system, to provide a homogeneous blend in which the active agent is solubilized. The proportion of the hydrophilic domain and hydrophobic domain is selected to optimize, or maximize, solubility of active agent in the matrix.

Abstract: Methods, apparatus, and processor-readable storage media for automatically predicting device failure using machine learning techniques are provided herein. An example computer-implemented method includes obtaining telemetry data from at least one client device; predicting failure of at least a portion of the at least one client device by processing at least a portion of the telemetry data using a first set of one or more machine learning techniques; predicting lifespan information pertaining to at least a portion of the at least one client device by processing the predicted failure and at least a portion of the telemetry data using a second set of one or more machine learning techniques; and performing at least one automated action based at least in part on one or more of the predicted failure and the predicted lifespan information.

Abstract: One example method, includes checking, by an intelligent time detector service running on a computing system, a trial period duration of software on the computing system, starting, by the intelligent time detector service, a counter for the software, and the counter is operable to generate a counter value based on a clock of the computing system, storing, by the intelligent time detector service, a counter value of the counter, and decommissioning, by the intelligent time detector service, the software after the trial period has expired.

Abstract: An apparatus comprises a processing device configured to identify a given one of one or more assets in an information technology infrastructure associated with a support indicator associated with a priority level, and to obtain information characterizing state transitions of the one or more assets. The processing device is also configured to determine, based at least in part on a current state of the given asset identified utilizing the obtained information, a probability of the given asset transitioning to each of a plurality of states and to select, based at least in part on the determined probabilities, one of the plurality of states as a predicted future state of the given asset utilizing conformal prediction. The processing device is further configured to modify the priority level of the support indicator associated with the given asset based at least in part on the predicted future state of the given asset.