Abstract: Systems and methods are provided to for identifying a mis-set, mis-calibrated, or malfunctioning thermostat. The method can include receiving ambient environmental data from at least one sensor monitoring an asset; identifying aberrative behavior in the ambient environment data; obtaining a complement of the aberrative behavior; determining a segment of normal behavior in the complement; identifying a mis-set subsequence in the segment; generating a report documenting the mis-set subsequence; and transmitting the report to a user device.

Abstract: A computing system may create an anomaly detection model to detect anomalies in multivariate data originating from a given data source by extracting a model object for the anomaly detection model using a first set of training data originating from the given data source, establishing starting values of a set of anomaly thresholds for the anomaly detection model using the extracted model object and a second set of training data originating from the given data source, and refining the starting values of the set of anomaly thresholds for at least a subset of the variables included in the multivariate data using the extracted model object and a set of test data. In turn, the computing system may use the anomaly detection model to monitor for anomalies in observation data originating from the given data source.

Abstract: A computing system may create an anomaly detection model to detect anomalies in multivariate data originating from a given data source by extracting a model object for the anomaly detection model using a first set of training data originating from the given data source, establishing starting values of a set of anomaly thresholds for the anomaly detection model using the extracted model object and a second set of training data originating from the given data source, and refining the starting values of the set of anomaly thresholds for at least a subset of the variables included in the multivariate data using the extracted model object and a set of test data. In turn, the computing system may use the anomaly detection model to monitor for anomalies in observation data originating from the given data source.

Abstract: A computing system may operate in a first mode during which it calculates a set of training metrics on a running basis as a stream of multivariate data points originating from a data source is being received. While operating in the first mode, the computing system may determine that the set of training metrics has reached a threshold level of stability. In response, the computing system may transition to a second mode during which its extracts a model object and calculates a set of model parameters for an anomaly detection model. While operating in the second mode, the computing system may determine that the set of model parameters has reached a threshold level of stability. In response, the computing system may transition to a third mode during which it uses the anomaly detection model to monitor for anomalies in the stream of multivariate data points originating from the data source.

Abstract: A method including receiving data including first searchable data segments and non-searchable data segments, identifying the non-searchable data segments within the data, determining coordinates for the non-searchable data segments relative to the first searchable data segments, extracting the non-searchable data segments, processing the non-searchable data segments, the processing including converting the non-searchable data segments into second searchable data segments, overlaying the second searchable data segments at the determined coordinates relative to the first searchable data segments and exporting the first searchable data segments and the second searchable data segments.

Abstract: The present invention provides a nucleic acid molecule encoding a phytase. The present invention also provides a nucleic acid molecule encoding a pH 2.5 acid phosphatase. Also provided are vectors, host cells, and a method of overexpressing phytate degrading enzymes.

Abstract: A highly efficient overexpression system for phytase and pH 2.5 acid phosphatase in Trichoderma is described. This system results in enzyme compositions that are especially useful in the animal feed industry.