Abstract: A building management system includes connected equipment configured to measure a plurality of monitored variables and a predictive diagnostics system configured to receive the monitored variables from the connected equipment; generate a probability distribution of the plurality of monitored variables; determine a boundary for the probability distribution using a supervised machine learning technique to separate normal conditions from faulty conditions indicated by the plurality of monitored variables; separate the faulty conditions into sub-patterns using an unsupervised machine learning technique to generate a fault prediction model, each sub-pattern corresponding with a fault, and each fault associated with a fault diagnosis; receive a current set of the monitored variables from the connected equipment; determine whether the current set of monitored variables correspond with one of the sub-patterns of the fault prediction model to facilitate predicting whether a corresponding fault will occur; and determin

Abstract: Heating apparatuses and methods for glass tubing manufacturing are disclosed. A heating apparatus for glass tubing manufacturing includes a bowl configured to receive molten glass and a plurality of heating elements thermally coupled to the bowl. The bowl has a bowl height and includes a tub portion configured to hold the molten glass, a bowl well extending beneath the tub portion, and an orifice at a distal end of the bowl well. The plurality of heating elements include a first heating element disposed at a first vertical location along the bowl height, a second heating element disposed at a second vertical location along the bowl height, wherein the first vertical location is vertically spaced apart from the second vertical location.

Abstract: A building management system includes connected equipment configured to measure a plurality of monitored variables and a predictive diagnostics system configured to receive the monitored variables from the connected equipment; generate a probability distribution of the plurality of monitored variables; determine a boundary for the probability distribution using a supervised machine learning technique to separate normal conditions from faulty conditions indicated by the plurality of monitored variables; separate the faulty conditions into sub-patterns using an unsupervised machine learning technique to generate a fault prediction model, each sub-pattern corresponding with a fault, and each fault associated with a fault diagnosis; receive a current set of the monitored variables from the connected equipment; determine whether the current set of monitored variables correspond with one of the sub-patterns of the fault prediction model to facilitate predicting whether a corresponding fault will occur; and determin

Abstract: A building management system includes a plurality of devices of building equipment configured to provide status data. The building management system also includes an equipment management server configured to assign and store parent-child relationships for the plurality of devices of building equipment. The equipment management server is also configured to monitor the status data to detect faults and generate a fault visualization interface. The fault visualization interface provides provide a list of the devices with detected faults, allows a user to select one or more of the devices from the list, and presents a parent-child relationship widget for the selected device. The parent-child relationship widget includes a list of parent devices for the selected device and a list of child devices for the selected device. Each device on the lists of parent devices and child devices has a status indicator indicating whether the device is in a fault condition.

Abstract: A building management system includes connected equipment configured to measure a plurality of monitored variables and a predictive diagnostics system configured to receive the monitored variables from the connected equipment; generate a probability distribution of the plurality of monitored variables; determine a boundary for the probability distribution using a supervised machine learning technique to separate normal conditions from faulty conditions indicated by the plurality of monitored variables; separate the faulty conditions into sub-patterns using an unsupervised machine learning technique to generate a fault prediction model, each sub-pattern corresponding with a fault, and each fault associated with a fault diagnosis; receive a current set of the monitored variables from the connected equipment; determine whether the current set of monitored variables correspond with one of the sub-patterns of the fault prediction model to facilitate predicting whether a corresponding fault will occur; and determin

Abstract: A building management system includes connected equipment configured to measure a plurality of monitored variables and a predictive diagnostics system configured to receive the monitored variables from the connected equipment; generate a probability distribution of the plurality of monitored variables; determine a boundary for the probability distribution using a supervised machine learning technique to separate normal conditions from faulty conditions indicated by the plurality of monitored variables; separate the faulty conditions into sub-patterns using an unsupervised machine learning technique to generate a fault prediction model, each sub-pattern corresponding with a fault, and each fault associated with a fault diagnosis; receive a current set of the monitored variables from the connected equipment; determine whether the current set of monitored variables correspond with one of the sub-patterns of the fault prediction model to facilitate predicting whether a corresponding fault will occur; and determin

Abstract: A building management system includes connected equipment configured to measure a plurality of monitored variables and a predictive diagnostics system configured to receive the monitored variables from the connected equipment; generate a probability distribution of the plurality of monitored variables; determine a boundary for the probability distribution using a supervised machine learning technique to separate normal conditions from faulty conditions indicated by the plurality of monitored variables; separate the faulty conditions into sub-patterns using an unsupervised machine learning technique to generate a fault prediction model, each sub-pattern corresponding with a fault, and each fault associated with a fault diagnosis; receive a current set of the monitored variables from the connected equipment; determine whether the current set of monitored variables correspond with one of the sub-patterns of the fault prediction model to facilitate predicting whether a corresponding fault will occur; and determin

Abstract: A building management system includes building equipment, a metric generation system, and a visualization engine. The building equipment is configured to generate data samples. The metric generation system is configured to collect the data samples and generate key performance indicators. The visualization engine is configured to create a scorecard that displays the key performance indicators. The scorecard comprises a building energy overview widget that displays total consumption of a building, a consumption by space widget that displays consumption of a plurality of subspaces of the building, and a consumption by commodity widget that displays consumption of the building categorized by commodity. In some embodiments, the scorecard also includes an energy density by space widget that displays consumption per unit area per day for the subspaces of the building.

Abstract: A building management system includes building equipment, a metric generation system, and a visualization engine. The building equipment is configured to generate data samples. The metric generation system is configured to collect the data samples and generate key performance indicators. The visualization engine is configured to create a scorecard that displays the key performance indicators. The scorecard comprises a building energy overview widget that displays total consumption of a building, a consumption by space widget that displays consumption of a plurality of subspaces of the building, and a consumption by commodity widget that displays consumption of the building categorized by commodity. In some embodiments, the scorecard also includes an energy density by space widget that displays consumption per unit area per day for the subspaces of the building.

Abstract: A building management system includes a plurality of devices of building equipment configured to provide status data. The building management system also includes an equipment management server configured to assign and store parent-child relationships for the plurality of devices of building equipment. The equipment management server is also configured to monitor the status data to detect faults and generate a fault visualization interface. The fault visualization interface provides provide a list of the devices with detected faults, allows a user to select one or more of the devices from the list, and presents a parent-child relationship widget for the selected device. The parent-child relationship widget includes a list of parent devices for the selected device and a list of child devices for the selected device. Each device on the lists of parent devices and child devices has a status indicator indicating whether the device is in a fault condition.

Abstract: A building management system includes connected equipment configured to measure a plurality of monitored variables and a predictive diagnostics system configured to receive the monitored variables from the connected equipment; generate a probability distribution of the plurality of monitored variables; determine a boundary for the probability distribution using a supervised machine learning technique to separate normal conditions from faulty conditions indicated by the plurality of monitored variables; separate the faulty conditions into sub-patterns using an unsupervised machine learning technique to generate a fault prediction model, each sub-pattern corresponding with a fault, and each fault associated with a fault diagnosis; receive a current set of the monitored variables from the connected equipment; determine whether the current set of monitored variables correspond with one of the sub-patterns of the fault prediction model to facilitate predicting whether a corresponding fault will occur; and determin

Abstract: Heating apparatuses and methods for glass tubing manufacturing are disclosed. A heating apparatus for glass tubing manufacturing includes a bowl configured to receive molten glass and a plurality of heating elements thermally coupled to the bowl. The bowl has a bowl height and includes a tub portion configured to hold the molten glass, a bowl well extending beneath the tub portion, and an orifice at a distal end of the bowl well. The plurality of heating elements include a first heating element disposed at a first vertical location along the bowl height, a second heating element disposed at a second vertical location along the bowl height, wherein the first vertical location is vertically spaced apart from the second vertical location.

Abstract: The invention relates to the gene therapeutic treatment of cancer using co-expressed nucleic acids encoding US28 and a G-protein. e.g. GNA-13, or functional fragments thereof, or using nucleic acids encoding fusion polypeptides of US28 and a G-protein or functional fragments thereof. The pharmaceutical compositions according to the invention are used in the treatment of cancer patients to induce apoptosis in tumor cells.

Abstract: A method and system for resolving access to a common resource for competing processes. According to one embodiment, a resource manager receives a request from a first process to access a resource, receives a request from a second process to access the resource, the request from the second process arriving after the request from the first process, grants access to the resource to the first process, queues the access request from the second process until the resource is released by the first process, and notifies the second process that its access request has been queued, wherein upon receiving the notification, the second process resumes operation as if the second process had been granted access to and released the resource.