Abstract: A control device for reducing and homogenizing welding residual stress by acoustic waves, comprising: a fixing tool having two rows of through holes; a plurality of ultrasonic transducers each having a body and a conical horn transmitting portion at a lower end of the body, wherein lower ends of the horn transmitting portions are fixedly connected with flanges, the horn transmitting portions of the ultrasonic transducers extend into respective through holes of the fixing tool to be in contact with welded parts below the fixing tool, the flanges are fixed to the fixing tool by bolts, and the two rows of through holes are arranged both sides of a welding seam of the welded parts; and a driving device for driving the ultrasonic transducers to operate. A corresponding control method is also provided. The control device can be used to control residual stress at the welding seam of steels.

Abstract: A system described herein may provide a technique for using modeling techniques to identify events, trends, etc. in a set of data, such as streaming video or audio content. The system may perform lightweight pre-processing operations on a different set of data, such as object position data, to identify timeframes at which an event may potentially have occurred, and the modeling techniques may be performed at portions of the streaming content that correspond to such timeframes. The system may forgo performing such modeling techniques at other portions of the streaming content, thus conserving processing resources.

Abstract: A device may include a processor configured to collect historical location data for a user equipment (UE) device associated with a user and determine a movement pattern for the user based on the collected historical location data. The processor may be further configured to obtain current location data for the UE device over a time period; determine a mobility score for the user for the time period based on the determined movement pattern and the obtained current location data, wherein the mobility score is associated with the user's movements during the time period with respect to the determined movement pattern for the user; and provide the determined mobility score for the user to another device configured to adjust one or more parameters of a radio access network (RAN) associated with the UE device based on the determined mobility score.

Abstract: A device may include a processor configured to collect historical location data for a user equipment (UE) device associated with a user and determine a movement pattern for the user based on the collected historical location data. The processor may be further configured to obtain current location data for the UE device over a time period; determine a mobility score for the user for the time period based on the determined movement pattern and the obtained current location data, wherein the mobility score is associated with the user's movements during the time period with respect to the determined movement pattern for the user; and provide the determined mobility score for the user to another device configured to adjust one or more parameters of a radio access network (RAN) associated with the UE device based on the determined mobility score.

Abstract: A device may collect network performance data associated with a user equipment of a network. The network performance data may include information associated with a plurality of performance indicators of the network. The device may process information associated with a first portion of the plurality of performance indicators to determine a first performance category experience score, and information associated with a second portion of the plurality of performance indicators to determine a second performance category experience score. The device may process the first performance category experience score and the second performance category experience score to determine a network experience score. The device may determine whether the network experience score satisfies a threshold value. The device may perform one or more actions based on determining that the network experience score satisfies the threshold value.

Abstract: A system may collect, from a wireless network, first data pertaining to nodes in the wireless network. Each datum of the first data belongs to one of two or more categories/For each of the nodes, for each of the categories, and for each datum belonging to the category, the system may determine if the datum is outside of a first range of values, and if the datum is inside the first range, the system may calculate a first base network performance health (NPH) score that is a function of the nodes, the categories, the data, and time. The system may also apply first deep learning to a first neural network among a plurality of neural networks to update first coefficients for correlating the first base NPH score to a mean opinion score, for each of the categories.

Abstract: A device may collect network performance data associated with a user equipment of a network. The network performance data may include information associated with a plurality of performance indicators of the network. The device may process information associated with a first portion of the plurality of performance indicators to determine a first performance category experience score, and information associated with a second portion of the plurality of performance indicators to determine a second performance category experience score. The device may process the first performance category experience score and the second performance category experience score to determine a network experience score. The device may determine whether the network experience score satisfies a threshold value. The device may perform one or more actions based on determining that the network experience score satisfies the threshold value.

Abstract: A device may receive cell property data associated with a cell in a mobile network and performance data associated with the cell. The device may determine whether the performance data associated with the cell satisfies a performance threshold. The device may identify, based on determining that the performance data associated with the cell satisfies the performance threshold, one or more impacted cells, in the mobile network, associated with the cell. The device may determine one or more antenna adjustment parameters based on at least the cell property data associated with the cell, the performance data associated with the cell, and performance data associated with the one or more impacted cells. The device may perform, based on the one or more antenna adjustment parameters, an action in connection with at least one of an antenna associated with the cell or another antenna associated with the one or more impacted cells.

Abstract: A system may collect, from a wireless network, first data pertaining to nodes in the wireless network. Each datum of the first data belongs to one of two or more categories/For each of the nodes, for each of the categories, and for each datum belonging to the category, the system may determine if the datum is outside of a first range of values, and if the datum is inside the first range, the system may calculate a first base network performance health (NPH) score that is a function of the nodes, the categories, the data, and time. The system may also apply first deep learning to a first neural network among a plurality of neural networks to update first coefficients for correlating the first base NPH score to a mean opinion score, for each of the categories.

Abstract: A system may collect, from a wireless network, first data pertaining to nodes in the wireless network. Each datum of the first data belongs to one of two or more categories/For each of the nodes, for each of the categories, and for each datum belonging to the category, the system may determine if the datum is outside of a first range of values, and if the datum is inside the first range, the system may calculate a first base network performance health (NPH) score that is a function of the nodes, the categories, the data, and time. The system may also apply first deep learning to a first neural network among a plurality of neural networks to update first coefficients for correlating the first base NPH score to a mean opinion score, for each of the categories.

Abstract: A device may receive cell property data associated with a cell in a mobile network and performance data associated with the cell. The device may determine whether the performance data associated with the cell satisfies a performance threshold. The device may identify, based on determining that the performance data associated with the cell satisfies the performance threshold, one or more impacted cells, in the mobile network, associated with the cell. The device may determine one or more antenna adjustment parameters based on at least the cell property data associated with the cell, the performance data associated with the cell, and performance data associated with the one or more impacted cells. The device may perform, based on the one or more antenna adjustment parameters, an action in connection with at least one of an antenna associated with the cell or another antenna associated with the one or more impacted cells.

Abstract: A system may collect, from a wireless network, first data pertaining to nodes in the wireless network. Each datum of the first data belongs to one of two or more categories/For each of the nodes, for each of the categories, and for each datum belonging to the category, the system may determine if the datum is outside of a first range of values, and if the datum is inside the first range, the system may calculate a first base network performance health (NPH) score that is a function of the nodes, the categories, the data, and time. The system may also apply first deep learning to a first neural network among a plurality of neural networks to update first coefficients for correlating the first base NPH score to a mean opinion score, for each of the categories.

Abstract: The present invention is related to compositions useful for the measurement of free or unbound analyte concentrations in a fluid. The present invention includes the use of capture ligands and stabilizing agents to improve the accuracy of analyte concentration assays. Methods and tools for using the present invention are also disclosed.

Abstract: The present invention is related to compositions useful for the measurement of free or unbound analyte concentrations in a fluid. The present invention includes the use of capture ligands and stabilizing agents to improve the accuracy of analyte concentration assays. Methods and tools for using the present invention are also disclosed.

Abstract: The present invention is related to compositions useful for the measurement of free or unbound analyte concentrations in a fluid. The present invention includes the use of capture ligands and stabilizing agents to improve the accuracy of analyte concentration assays. Methods and tools for using the present invention are also disclosed.

Abstract: The present invention is related to compositions useful for the measurement of free or unbound analyte concentrations in a fluid. The present invention includes the use of capture ligands and stabilizing agents to improve the accuracy of analyte concentration assays. Methods and tools for using the present invention are also disclosed.

Abstract: Devices are useful to capture or dispense target molecules and include an electric field-responsive valve unit. The valve unit includes an active control structure having at least one monomolecular layer and being up to about 10 nm thick. The active control structure is formed by a majority of molecular species with a dipolar moment greater than about 5 Debye, and operable in response to an electric field at a threshold value. A particularly preferred embodiment has the active control structure carried on a microporous membrane with pores in the nanometer range. Applications for inventive embodiments include drug delivery and target molecule capture during electrophoresis separations.