Abstract: Aspects of the subject disclosure may include, for example, receiving a handover request from a user equipment (UE) device in a mobility network, wherein the handover request identifies a target cell for handing over radio communication with the UE device from a source cell, wherein the identifying is based on the handover request, determining a usage level of the UE device, wherein the usage level comprises one of performance-sensitive traffic and performance-tolerant traffic, selecting an alternative target cell for the handover request, wherein the selecting is responsive to determining a performance-sensitive traffic usage level of the UE device, and initiating a handover operation between the source cell and the alternative target cell. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, training a first machine learning model based on a combination of labeled training data and unlabeled training data, the first machine learning model producing augmented training data, training a second machine learning model based on a combination of the labeled training data and the augmented training data, receiving, at a client device, customer information about a service degradation at a user equipment (UE) device of a customer in a cellular network, providing the customer information to the second machine learning model, receiving, at the client device, from the second machine learning model, information identifying a root cause of the service degradation, and modifying a network component of the cellular network or the UE device, based on the information identifying a root cause of the service degradation. Other embodiments are disclosed.

Abstract: The present disclosure relates to a technical field for airborne navigation and discloses a data acquisition system and method for airborne navigation devices based on unmanned aerial vehicle. The system includes an unmanned aerial vehicle flight control system, a navigation devices test antenna array, a multi-channel signal processing module, a signal acquisition module, an ADS-B transmitting module, a GNSS receiver, a UHF data link receiver, a power module and a ground station. The unmanned aerial vehicle is equipped with corresponding modules to receive signals from ground navigation devices, perform corresponding processing and storage, and transmit data to the ground, at the same time, receive control instructions sent by the ground to complete corresponding monitoring, analysis and inspection.

Abstract: Aspects of the subject disclosure may include, for example, training a cell-level machine learning model to predict a likelihood of a cell site in a cellular network having service issues that impact customers of the cellular network, training a user equipment (UE) level machine learning model using output information from the cell-level machine learning model and historical information about UE-level performance metrics, receiving, from a customer associated with a UE device operating on the cellular network, information about a service degradation experienced by the customer on the UE device, providing the information about the service degradation to the UE-level machine learning model; and receiving, from the UE-level machine learning model, information identifying a source of the service degradation. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, categorizing users of a cellular network according to a plurality of user categories, identifying, by a machine learning model, a service degradation in the cellular network, identifying at least one affected user, the at least one affected user being affected by the service degradation, identifying one or more affected user categories including the at least one affected user, identifying potentially affected users, the potentially affected users being categorized according to the one or more affected user categories, and taking action to isolate the potentially affected users from the service degradation. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, receiving, from a machine learning model, information about an event causing a service degradation in a cellular network, wherein the event is external to the cellular network, determining one or more event categories associated with the event causing the service degradation, determining, based on the one or more event categories, likely affected customers, the likely affected customers being likely to experience the service degradation, determining, by the machine learning model, proper resources for resolution of the service degradation, wherein the determining proper resources is based on the one or more event categories, and dispatching the proper resources for resolution of the service degradation. Other embodiments are disclosed.

Abstract: The present disclosure provides an unmanned aerial vehicle platform based vision measurement method for a static rigid object. Aiming at the problem of high professionality but poor versatility of existing vision measurement methods, the present disclosure uses a method combining object detection and three-dimensional reconstruction to mark an object to be measured, and uses a three-dimensional point cloud processing method to further mark a size to be measured and calculate its length, which takes full advantage of the convenience of data collection by an unmanned aerial vehicle platform (UAV), and its global navigation satellite system (GNSS), an inertial measurement unit (IMU) and the like to assist measurement. There is no need to use common auxiliary devices such as a light pen and a marker, which can improve the versatility of vision measurement.

Abstract: The present disclosure relates to a technical field for airborne navigation and discloses a data acquisition system and method for airborne navigation devices based on unmanned aerial vehicle. The system includes an unmanned aerial vehicle flight control system, a navigation devices test antenna array, a multi-channel signal processing module, a signal acquisition module, an ADS-B transmitting module, a GNSS receiver, a UHF data link receiver, a power module and a ground station. The unmanned aerial vehicle is equipped with corresponding modules to receive signals from ground navigation devices, perform corresponding processing and storage, and transmit data to the ground, at the same time, receive control instructions sent by the ground to complete corresponding monitoring, analysis and inspection.

Abstract: For device configuration and monitoring, a processor receives configuration settings from an electronic device via the electronic device interface. The processor converts the configuration settings to hardware settings for a target device. The processor communicates the hardware settings to the target device via a target device interface.

Abstract: The present disclosure provides an unmanned aerial vehicle platform based vision measurement method for a static rigid object. Aiming at the problem of high professionality but poor versatility of existing vision measurement methods, the present disclosure uses a method combining object detection and three-dimensional reconstruction to mark an object to be measured, and uses a three-dimensional point cloud processing method to further mark a size to be measured and calculate its length, which takes full advantage of the convenience of data collection by an unmanned aerial vehicle platform (UAV), and its global navigation satellite system (GNSS), an inertial measurement unit (IMU) and the like to assist measurement. There is no need to use common auxiliary devices such as a light pen and a marker, which can improve the versatility of vision measurement.

Abstract: For device configuration and monitoring, a processor receives configuration settings from an electronic device via the electronic device interface. The processor converts the configuration settings to hardware settings for a target device. The processor communicates the hardware settings to the target device via a target device interface.

Abstract: Methods of capturing N-glycan linked glycomolecules including N-glycans, N-glycopeptides and N-glycoproteins are described. The methods provide substantially unbiased capture of charged and uncharged N-glycans and/or N-glycan linked glycomoleules. Binding reagents for substantially unbiased binding of N-glycans and/or N-glycan linked glycomolecules are also described.

Abstract: Provided herein is an ?-fucosidase that can cleave a conjugate comprising an N-glycan and a label where the label is added by amine reactive chemistry. The ?-fucosidase also has an accelerated reaction time using Schiff base labeled N-glycans compared with bovine kidney fucosidase. A reaction mix, enzyme mix and kit comprising the ?-fucosidase are provided, as well as a method for analyzing glycoproteins. The ?-fucosidase finds particular use in analyzing the N-glycans of therapeutic glycoproteins.

Abstract: Methods of capturing N-glycan linked glycomolecules including N-glycans, N-glycopeptides and N-glycoproteins are described. The methods provide substantially unbiased capture of charged and uncharged N-glycans and/or N-glycan linked glycomoleules. Binding reagents for substantially unbiased binding of N-glycans and/or N-glycan linked glycomolecules are also described.

Abstract: Methods of capturing N-glycan linked glycomolecules including N-glycans, N-glycopeptides and N-glycoproteins are described. The methods provide substantially unbiased capture of charged and uncharged N-glycans and/or N-glycan linked glycomolecules. Binding reagents for substantially unbiased binding of N-glycans and/or N-glycan linked glycomolecules are also described.

Abstract: Provided herein is an ?-fucosidase that can cleave a conjugate comprising an N-glycan and a label where the label is added by amine reactive chemistry. The ?-fucosidase also has an accelerated reaction time using Schiff base labeled N-glycans compared with bovine kidney fucosidase. A reaction mix, enzyme mix and kit comprising the ?-fucosidase are provided, as well as a method for analyzing glycoproteins. The ?-fucosidase finds particular use in analyzing the N-glycans of therapeutic glycoproteins.

Abstract: Provided herein is an ?-fucosidase that can cleave a conjugate comprising an N-glycan and a label where the label is added by amine reactive chemistry. The ?-fucosidase also has an accelerated reaction time using Schiff base labeled N-glycans compared with BKF. A reaction mix, enzyme mix and kit comprising the ?-fucosidase are provided, as well as a method for analyzing glycoproteins. The ?-fucosidase finds particular use in analyzing the N-glycans of therapeutic glycoproteins.

Abstract: The embodiments of the present invention provide a method for processing information, comprising: obtaining attribute information about a target field; obtaining, according to the attribute information about the target field, attribute information about the target field described in a first language; and sending the attribute information about the target field described in the first language to a query engine using the first language, so that the query engine obtains, according to the attribute information about the target field described in the first language, data corresponding to the target field; and the embodiments of the present invention also provide an apparatus for processing information. According to the technical solutions provided in the embodiments of the present invention, attribute information about a target field being automatically described in a language can be realized, so as to improve the query efficiency of data.