Abstract: Diabetes prediction using glucose measurements and machine learning is described. In one or more implementations, the observation analysis platform includes a machine learning model trained using historical glucose measurements and historical outcome data of a user population to predict a diabetes classification for an individual user. The historical glucose measurements of the user population may be provided by glucose monitoring devices worn by users of the user population, while the historical outcome data includes one or more diagnostic measurements obtained from sources independent of the glucose monitoring devices. Once trained, the machine learning model predicts a diabetes classification for a user based on glucose measurements collected by a wearable glucose monitoring device during an observation period spanning multiple days. The predicted diabetes classification may then be output, such as by generating one or more notifications or user interfaces based on the classification.

Abstract: A crank arm of an auxiliary rotary switch in a circuit breaker changes electrical connections of contacts in the auxiliary rotary switch when the crank-arm is rotated about its axis. An auxiliary switch actuator decouples abrupt forces from being applied to the crank arm resulting from closing main contacts of the circuit breaker. In response to the main contacts starting to close, the crank arm is set into rotation by motion of a connection-state link that is coupled to the main contacts. The rotation of the crank arm continues up to a point at which the rotation is stopped, while the connection-state link continues its motion without being connected to the crank arm. In this manner, the connection-state link is decoupled from the crank arm, to relieve the crank arm from receiving the abrupt forces conducted by the connection-state link resulting from the main circuit breaker contacts closing.

Abstract: A grounding system for a rackable circuit breaker includes a finger cluster connector connected to a grounding bus in the circuit breaker and a rail conductor mounted in a circuit breaker cradle and connected to ground potential. The finger cluster connector includes a conductor formed from a single plate of conductive material folded into a generally U-shaped body with first and second sides divided into a plurality of fingers. Distal ends of the fingers form mutually facing parallel surfaces of a constricted passage configured to make a sliding electrical contact with the rail conductor that is advanced into the constricted passage as the circuit breaker is racked into the cradle. First and second leaf springs fastened to the conductor portion are divided into a plurality of spring finger sections with spring distal ends configured to respectively press against the distal ends of respective fingers of the conductor portion.

Abstract: A boot for preventing unintended contact with live electrical components of a medium voltage control device. The boot having cover for accessing mechanical fasteners used to connect electrical conductors to control equipment when periodic maintenance is required.

Abstract: Methods and apparatus, including computer program products, are provided for remote monitoring. In some example implementations, there is provided a method. The method may include receiving, at a remote monitor, a notification message representative of an event detected, by a server, from analyte sensor data obtained from a receiver monitoring an analyte state of a host; presenting, at the remote monitor, the notification message to activate the remote monitor, wherein the remote monitor is configured by the server to receive the notification message to augment the receiver monitoring of the analyte state of the host; accessing, by the remote monitor, the server, in response to the presenting of the notification message; and receiving, in response to the accessing, information including at least the analyte sensor data. Related systems, methods, and articles of manufacture are also disclosed.

Abstract: Wireless mesh network (WMN) architectures of network hardware devices organized in a mesh topology is described. One device communicates, using a first radio, first data with a second device via a first wireless link between the device and the second device. The device communicates, using a second radio, second data with a third device via a second wireless link between the device and the third device. The device communicates, using a third radio, third data with a fourth device via a third wireless link between the device and the fourth device. The device communicates, using a fourth radio, fourth data with a server of a content delivery network (CDN) via a point-to-point wireless link between the device and the server. The device is an only ingress point for content files for a mesh network that includes at least the device, the second device, and the third device.

Abstract: A method of enabling remote audience participation at a live venue event includes receiving, at an aggregator, media inputs from one or more user device of one or more users in a subscription roster for participation in a live event; generating an aggregated media stream by processing the media inputs; and forwarding the aggregated media stream to a venue of the live event.

Abstract: Provided are systems and methods using which users may learn and become familiar with the effects of various aspects of their lifestyle on their health, e.g., users may learn about how food and/or exercise affects their glucose level and other physiological parameters, as well as overall health. In some cases the user selects a program to try; in other cases, a computing environment embodying the system suggests programs to try, including on the basis of pattern recognition, i.e., by the computing environment determining how a user could improve a detected pattern in some way. In this way, users such as type II diabetics or even users who are only prediabetic or non-diabetic may learn healthy habits to benefit their health.

Abstract: Diabetes prediction using glucose measurements and machine learning is described. In one or more implementations, the observation analysis platform includes a machine learning model trained using historical glucose measurements and historical outcome data of a user population to predict a diabetes classification for an individual user. The historical glucose measurements of the user population may be provided by glucose monitoring devices worn by users of the user population, while the historical outcome data includes one or more diagnostic measurements obtained from sources independent of the glucose monitoring devices. Once trained, the machine learning model predicts a diabetes classification for a user based on glucose measurements collected by a wearable glucose monitoring device during an observation period spanning multiple days. The predicted diabetes classification may then be output, such as by generating one or more notifications or user interfaces based on the classification.

Abstract: An ultrasonic surgical apparatus including a first signal generator outputting a drive signal at a predetermined voltage and frequency, a first oscillating structure receiving the drive signal and oscillating at the frequency of the drive signal, and a bridge circuit, detecting the mechanical motion of the first oscillating structure and outputting a signal representative of the mechanical motion.

Abstract: In one embodiment, a processing device receives a request to claim ownership of a first hosted media item on a first media sharing platform, wherein the request is received from a rights holder that holds one or more rights to a claimed media item that is incorporated into the first hosted media item. The processing device adds an indicator of the ownership of the rights holder to a first entry for the first hosted media item in a data store. The processing device determines that a second hosted media item having a threshold similarity to the first hosted media item is hosted by a second media sharing platform. The processing device automatically adds an indicator of the ownership of the rights holder to a second entry for the second hosted media item without receiving a separate request to claim ownership of the second hosted media item.

Abstract: A molded member containing a thermoplastic material and a substrate, wherein at least a portion of the substrate is coupled to the thermoplastic material via a silicon-containing linker, and a respiratory apparatus containing the molded member are disclosed. A respiratory apparatus containing a molded member, wherein the molded member contains a thermosetting material and a substrate, and wherein at least a portion of the substrate is coupled to the thermosetting material via a silicon-containing linker is also disclosed.

Abstract: A working machine having a base assembly including a ground engaging structure; an undercarriage connected to the ground engaging structure; a superstructure connected to the undercarriage; a working arm mounted to the superstructure; a connector connecting the undercarriage to the superstructure; and a drive arrangement located in the base assembly for moving the ground engaging structure to propel, in use, the working machine.

Abstract: Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.

Abstract: Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.

Abstract: Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.

Abstract: Machine learning in an artificial pancreas is described. An artificial pancreas system may include a wearable glucose monitoring device, an insulin delivery system, and a computing device. Broadly speaking, the wearable glucose monitoring device provides glucose measurements of a person continuously. The artificial pancreas algorithm, which may be implemented at the computing device, determines doses of insulin to deliver to the person based on a variety of aspects for the purpose of maintaining the person's glucose within a target range, as indicated by those glucose measurements. The insulin delivery system then delivers those determined doses to the person. As the artificial pancreas algorithm determines insulin doses for the person over time and effectiveness of the insulin doses to maintain the person's glucose level in the target range is observed, an underlying model of the artificial pancreas algorithm may be updated to better determine insulin doses.