Abstract: In several aspects, a processor receives a set of parameters characterizing sensor data from a plurality of sensors corresponding to drilling operations of a drill assembly for boring the Earth, and a condition of the drill assembly. The processor selects, by a drill assembly machine learning model executed by the processor, a subset of parameters that are related to the condition of the drill assembly. The drill assembly machine learning model uses an historical record of the set of parameters and a set of conditions of the drill assembly collected over time to determine whether a parameter is related to each condition. The processor applies weights to each parameter of the subset of parameters and weights to different levels of each parameter of the subset of parameters, producing a weighted subset of parameters and outputting the weighted subset of parameters.

Abstract: Systems and methods for democratizing compliance in an enterprise. A system receives a selection of a project in the enterprise and a compliance type, causes a list of controls associated with the compliance type to be displayed on a user interface, receives a set of configurations for each of the list of controls from the user, dynamically generates a schema based on the compliance type, the list of controls, and the set of configurations, automatically triggers execution of validation of each of the list of controls for the compliance type based on the generated schema, generates results of the validation, the results including a list of non-compliant controls and a list of compliant controls. The execution of validation of the list of controls is retriggered until each of the list of controls corresponding to the compliance type is compliant.

Abstract: A system for guiding a drill assembly boring the Earth can include a drill controller engine that receives parameters characterizing sensor data from a plurality of sensors of the drill assembly, and a condition of the drill assembly. The parameters and condition can be aggregated into a historical record. A subset of the parameters can be selected based on a relationship between the subset of parameters and the condition of the drill assembly, the relationship being determined by a drill assembly machine learning model. A Damage Index (DI) can be calculated from the subset of parameters. The DI can be matched with a plurality of DIs computed for the historical record to determine a risk of failure of the drill assembly. The drilling operations of the drill assembly can be adjusted by the drill controller engine in response to the risk of failure.

Abstract: A system for guiding a drill assembly boring the Earth can include a drill controller engine that receives parameters characterizing sensor data from a plurality of sensors of the drill assembly, and a condition of the drill assembly. The parameters and condition can be aggregated into a historical record. A subset of the parameters can be selected based on a relationship between the subset of parameters and the condition of the drill assembly, the relationship being determined by a drill assembly machine learning model. A Damage Index (DI) can be calculated from the subset of parameters. The DI can be matched with a plurality of DIs computed for the historical record to determine a risk of failure of the drill assembly. The drilling operations of the drill assembly can be adjusted by the drill controller engine in response to the risk of failure.

Abstract: In some implementations, a device may generate, based at least in part on a first set of inputs, a serverless software development environment associated with a set of cloud resources. The device may generate, based at least in part on a first machine learning model, a technology stack recommendation having a set of associated tools for performing a software development task. The device may instantiate the selected technology stack in the serverless software development environment and generate a set of applications based at least in part on executing the set of tools. The device may deploy the set of applications in one or more serverless application environments. The device may use machine learning to observe deployed applications, detect hidden anomalies, and perform root-cause analysis, thereby providing a lean and sustainable serverless environment.

Abstract: To reduce power consumption, a user equipment (UE) may trigger a microsleep in a subframe when a physical downlink control channel (PDCCH) does not include a downlink grant. However, because the microsleep depends on the PDCCH not including a downlink grant, the UE cannot trigger the microsleep until after the PDCCH is decoded. Accordingly, in some aspects, a UE may enable an aggressive extra microsleep in which a microsleep duration may be increased by reducing a PDCCH decode time. For example, a UE may use a stale channel estimate and noise estimate from a previous subframe to perform PDCCH demapping when conditions related to a PDCCH block error rate are satisfied. In this way, removing channel estimation and noise estimation from a PDCCH demapping path may reduce the PDCCH decode time, whereby the UE may trigger a microsleep sooner to increase a sleep ratio and thereby increase power savings.

Abstract: A weapon usage monitoring system that monitors information about at least a first weapon and a second weapon includes a mobile networking hub, a first weapon response device, a second weapon response device and an edge compute resource. The mobile networking hub can provide a local area network (LAN). The first weapon response device can have a first sensor that senses first weapon response data about the first weapon. The first weapon response device can communicate the first weapon response data to the mobile networking hub. The second weapon response device can have a second sensor that senses second weapon response data about the second weapon. The second weapon response device can communicate the second weapon response data to the mobile networking hub. An edge compute resource can aggregate the first and second weapon response data from the mobile networking hub into aggregated weapon response data.

Abstract: Disclosed examples display a template mapping graphical user interface (GUI), the template mapping GUI indicative of services mappable to service templates; map a first service of the services to a first service template of the service templates; and generate an application blueprint based on the first service mapped to the first service template, the application blueprint to provide an option during a runtime phase for user-selection of the first service to be deployed based on the application blueprint.

Abstract: In some implementations, a device may generate, based at least in part on a first set of inputs, a serverless software development environment associated with a set of cloud resources. The device may generate, based at least in part on a first machine learning model, a technology stack recommendation having a set of associated tools for performing a software development task. The device may instantiate the selected technology stack in the serverless software development environment and generate a set of applications based at least in part on executing the set of tools. The device may deploy the set of applications in one or more serverless application environments. The device may use machine learning to observe deployed applications, detect hidden anomalies, and perform root-cause analysis, thereby providing a lean and sustainable serverless environment.

Abstract: An example apparatus includes means for generating an application blueprint during a design phase, the application blueprint to bind a service template to a node of an application to be deployed in a cloud environment, the service template to be mapped to a plurality of services that are selectable during a runtime phase; and means for generating a deployment profile during the runtime phase, the deployment profile generating means to: select a first service and a second service from the plurality of services mapped to the service template when the application is to be deployed in the cloud environment, the first service different from the second service; generate a first deployment profile to deploy a first instance of the application based on the first service; and generate a second deployment profile to deploy a second instance of the application based on the second service.

Abstract: To reduce power consumption, a user equipment (UE) may trigger a microsleep in a subframe when a physical downlink control channel (PDCCH) does not include a downlink grant. However, because the microsleep depends on the PDCCH not including a downlink grant, the UE cannot trigger the microsleep until after the PDCCH is decoded. Accordingly, in some aspects, a UE may enable an aggressive extra microsleep in which a microsleep duration may be increased by reducing a PDCCH decode time. For example, a UE may use a stale channel estimate and noise estimate from a previous subframe to perform PDCCH demapping when conditions related to a PDCCH block error rate are satisfied. In this way, removing channel estimation and noise estimation from a PDCCH demapping path may reduce the PDCCH decode time, whereby the UE may trigger a microsleep sooner to increase a sleep ratio and thereby increase power savings.

Abstract: An example apparatus includes means for generating an application blueprint during a design phase, the application blueprint to bind a service template to a node of an application to be deployed in a cloud environment, the service template to be mapped to a plurality of services that are selectable during a runtime phase; and means for generating a deployment profile during the runtime phase, the deployment profile generating means to: select a first service and a second service from the plurality of services mapped to the service template when the application is to be deployed in the cloud environment, the first service different from the second service; generate a first deployment profile to deploy a first instance of the application based on the first service; and generate a second deployment profile to deploy a second instance of the application based on the second service.

Abstract: The current document is directed to an automated-application-release-management subsystem that executes application-release-management pipelines that includes one or more tasks and/or stages, execution of which launch execution of scripts on specified host computers. The capability for launching scripts from application-release-management-pipeline tasks and stages provides flexibility in design and development of application-release-management pipelines and provides an incremental path for migration of legacy-script-based application-release management to automated pipeline-based application-release management.

Abstract: Exemplary embodiments of the present disclosure are directed towards an artificial intelligence platform and computer implemented method for collaborating, automating and organizing drugs/medical/consultations/health information & Services between stakeholders in the pharmaceutical/healthcare industries. This also extends to professionals in the veterinary space such as veterinarians and animal specialists. The medical information generation module comprising: prescription fulfillment module, collaboration module, health assistant module, analytics module, virtual pharmacy & consultation module, and prescription auction module. The prescription fulfillment module is configured to use medical information collected from a database and, with the use of artificial intelligence, display medical information data and additional supplements & services suggestions on computing devices based on users.

Abstract: A disclosed example to configure an application blueprint includes binding, during a design phase, a service template to a node of the application blueprint, the application blueprint corresponding to an application to be deployed, the service template mapped to a plurality of services to be selectable during a runtime phase to implement the node; and storing, during the design phase, the application blueprint for subsequent access during the runtime phase to generate a first deployment profile and a second deployment profile, the first deployment profile to deploy a first instance of the application based on a first service selected from the service template to implement the node, and the second deployment profile to deploy a second instance of the application based on a second service, different from the first service, selected from the service template to implement the node.

Abstract: A system and method for efficiently replacing clients running a large scale application uses a pool of clients, which includes a group of active clients running the large scale application and at least one standby client. When a particular active client is identified to be replaced, one of the at least one standby client is selected as a replacement client for the particular active client. The particular active client is replaced with the selected replacement client with respect to the running of the large scale application such that the particular client is removed from the group of active clients and the selected replacement client is added to the group of active clients.

Abstract: A server includes a network communication device, a storage device, and a processing device. The processing device executes computer-readable instructions that, when executed by the processing device, cause the processing device to: receive contextual data from a client computing device via the network communication device, the contextual data indicates a first application view displayed on the client computing device; identify a first application function ID that identifies a first application function corresponding with the first application view; determine a first user intent ID that corresponds with the first application function ID; determine a second user intent ID that transitions from the first user intent ID; identify a second application function ID that corresponds with the second user intent ID; retrieve a card object corresponding with the second application function ID from the storage device; and transmit the card object to the client computing device via the network communication device.

Abstract: A disclosed example to configure an application blueprint includes binding, during a design phase, a service template to a node of the application blueprint, the application blueprint corresponding to an application to be deployed, the service template mapped to a plurality of services to be selectable during a runtime phase to implement the node; and storing, during the design phase, the application blueprint for subsequent access during the runtime phase to generate a first deployment profile and a second deployment profile, the first deployment profile to deploy a first instance of the application based on a first service selected from the service template to implement the node, and the second deployment profile to deploy a second instance of the application based on a second service, different from the first service, selected from the service template to implement the node.

Abstract: Methods and apparatus are disclosed to scale application deployments in cloud computing environments using virtual machine pools. An example method disclosed herein includes preparing a virtual machine pool including a virtual machine for use in a scaling operation, the virtual machine prepared in accordance with a blueprint of the application deployed in a deployment environment separate from the virtual machine pool, in response to receiving a request to scale the application, determining by executing an instruction with a processor, whether configuration information of the virtual machine pool satisfies a scaling requirement included in the request, and based on the determination, executing an instruction with the processor to transfer the virtual machine from the virtual machine pool to the deployment environment to perform the scaling operation in accordance with the request to scale.

Abstract: Disclosed examples to configure an application blueprint involve selecting, during a runtime phase, a first service and a second service from a plurality of services mapped to a service template, the service template bound to a node by an application blueprint, the application blueprint generated during a design phase; generating, during the runtime phase, a first deployment profile to deploy a first application on the node in a cloud environment, the first deployment profile based on the application blueprint, the first deployment profile identifying the first service; and generating, during the runtime phase, a second deployment profile to deploy a second application on the node in the cloud environment based on the application blueprint, the second deployment profile identifying the second service.