Abstract: The present disclosure provides a modified protein sequence i.e., recombinantly expressed glycan binding protein. The said glycan binding protein is a variant of Sclerotium Rolfsii Lectin and is modified to include attributes like enhanced molecule stability, reduced N-terminal methionine impurities and aid in conjugation of protein to other biological and chemical agent(s).

Abstract: Methods, systems and computer program products implementing hierarchical classification techniques are disclosed. A hierarchical classification system receives training data including labeled transaction records. The system determines tag sequences from the training data. The system clusters the tag sequences into clusters. The system determines a cluster-level classifier that is trained to predict a cluster for an input transaction record. The system determines a respective cluster-specific classifier for each cluster. The system trains the cluster-specific classifier to predict a label of entity of interest for an input transaction record, given a particular cluster. Upon receiving a test transaction record, the system first applies the cluster-level classifier to determine a particular cluster for the test transaction record, and then determines a label of entity of interest of the test transaction record by applying a cluster-specific classifier of that particular cluster.

Abstract: Methods, systems and computer program products implementing hierarchical classification techniques are disclosed. A hierarchical classification system receives training data including labeled transaction records. The system determines tag sequences from the training data. The system clusters the tag sequences into clusters. The system determines a cluster-level classifier that is trained to predict a cluster for an input transaction record. The system determines a respective cluster-specific classifier for each cluster. The system trains the cluster-specific classifier to predict a label of entity of interest for an input transaction record, given a particular cluster. Upon receiving a test transaction record, the system first applies the cluster-level classifier to determine a particular cluster for the test transaction record, and then determines a label of entity of interest of the test transaction record by applying a cluster-specific classifier of that particular cluster.

Abstract: A meter case used with a utility meter is disclosed. The meter shell includes a plurality of snap fit joints assembled on the meter case arranged to engage a module cover to mount the module cover to the meter shell. The module cover further includes wire housing formed along an inner periphery of the module cover that houses a wire having a first end and a second end therein. A metal shield placed behind the meter shell is electrically coupled to a first connector. A terminal electrically connected to the metal shield is arranged to discharge current to ground. An electrical contact electrically coupled to the second end of the wire is arranged to engage the first connector to establish an electrical connection to the metal shield for the discharge to ground of electrostatic currents sensed by wire.

Abstract: Methods, systems and computer program products implementing hierarchical classification techniques are disclosed. A hierarchical classification system receives training data including labeled transaction records. The system determines tag sequences from the training data. The system clusters the tag sequences into clusters. The system determines a cluster-level classifier that is trained to predict a cluster for an input transaction record. The system determines a respective cluster-specific classifier for each cluster. The system trains the cluster-specific classifier to predict a label of entity of interest for an input transaction record, given a particular cluster. Upon receiving a test transaction record, the system first applies the cluster-level classifier to determine a particular cluster for the test transaction record, and then determines a label of entity of interest of the test transaction record by applying a cluster-specific classifier of that particular cluster.

Abstract: A meter case used with a utility meter is disclosed. The meter shell includes a plurality of snap fit joints assembled on the meter case arranged to engage a module cover to mount the module cover to the meter shell. The module cover further includes wire housing formed along an inner periphery of the module cover that houses a wire having a first end and a second end therein. A metal shield placed behind the meter shell is electrically coupled to a first connector. A terminal electrically connected to the metal shield is arranged to discharge current to ground. An electrical contact electrically coupled to the second end of the wire is arranged to engage the first connector to establish an electrical connection to the metal shield for the discharge to ground of electrostatic currents sensed by wire.

Abstract: The disclosure relates to a concurrent 3-Way bidirectional message redirection component of control system which includes two coordinating supervisors each configured to process data streams representing sensor values and local inputs from an environment and to return additional data as well as operating instructions to the source of the data stream. The message redirection component determines which coordinating supervisor is currently in control of the operation of the system and moderates transitions between configurations of the system.

Abstract: Methods, systems and computer program products implementing hierarchical classification techniques are disclosed. A hierarchical classification system receives training data including labeled transaction records. The system determines tag sequences from the training data. The system clusters the tag sequences into clusters. The system determines a cluster-level classifier that is trained to predict a cluster for an input transaction record. The system determines a respective cluster-specific classifier for each cluster. The system trains the cluster-specific classifier to predict a label of entity of interest for an input transaction record, given a particular cluster. Upon receiving a test transaction record, the system first applies the cluster-level classifier to determine a particular cluster for the test transaction record, and then determines a label of entity of interest of the test transaction record by applying a cluster-specific classifier of that particular cluster.