Abstract: Systems, methods, non-transitory computer readable media can be configured to access a plurality of sensor logs corresponding to a first machine, each sensor log spanning at least a first period; access first computer readable logs corresponding to the first machine, each computer readable log spanning at least the first period, the computer readable logs comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type; determine a set of statistical metrics derived from the sensor logs; determine a set of log metrics derived from the computer readable logs; and determine, using a risk model that receives the statistical metrics and log metrics as inputs, fault probabilities or risk scores indicative of one or more fault types occurring in the first machine within a second period.

Abstract: Sensor logs corresponding to a first machine are accessed. Each sensor log spans at least a first period. First computer readable logs corresponding to the first machine are accessed. Each computer readable log spans at least the first period, the computer readable logs include a maintenance log including maintenance task objects, each maintenance task object includes a time and a maintenance task type. A set of statistical metrics are derived from the sensor logs. A set of log metrics are derived from the computer readable logs. Using a risk model that receives the statistical metrics and log metrics as inputs, fault probabilities or risk scores indicative of one or more fault types occurring in the first machine within a second period are determined.

Abstract: Systems, methods, non-transitory computer readable media can be configured to access a plurality of sensor logs corresponding to a first machine, each sensor log spanning at least a first period; access first computer readable logs corresponding to the first machine, each computer readable log spanning at least the first period, the computer readable logs comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type; determine a set of statistical metrics derived from the sensor logs; determine a set of log metrics derived from the computer readable logs; and determine, using a risk model that receives the statistical metrics and log metrics as inputs, fault probabilities or risk scores indicative of one or more fault types occurring in the first machine within a second period.

Abstract: Systems, methods, non-transitory computer readable media can be configured to access a plurality of sensor logs corresponding to a first machine, each sensor log spanning at least a first period; access first computer readable logs corresponding to the first machine, each computer readable log spanning at least the first period, the computer readable logs comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type; determine a set of statistical metrics derived from the sensor logs; determine a set of log metrics derived from the computer readable logs; and determine, using a risk model that receives the statistical metrics and log metrics as inputs, fault probabilities or risk scores indicative of one or more fault types occurring in the first machine within a second period.

Abstract: Systems, methods, non-transitory computer readable media can be configured to accessing a target sensor log corresponding to a first machine; accessing one or more prior sensor logs corresponding to the first machine and one or more prior sensor logs corresponding to a plurality of second machines which are of the same type as the first machine; accessing a plurality of computer readable logs corresponding to the first machine and the second machines, the computer readable logs for each second machine comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type; determining a set of statistical metrics characterising a difference between the target sensor log and each prior sensor log; selecting a sub-set of the prior sensor logs in dependence upon the statistical metrics; analysing the maintenance logs to correlate each prior sensor log included in the subset to one or more correlated maintenance tasks; selecting a

Abstract: Systems, methods, non-transitory computer readable media can be configured to accessing a target sensor log corresponding to a first machine; accessing one or more prior sensor logs corresponding to the first machine and one or more prior sensor logs corresponding to a plurality of second machines which are of the same type as the first machine; accessing a plurality of computer readable logs corresponding to the first machine and the second machines, the computer readable logs for each second machine comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type; determining a set of statistical metrics characterising a difference between the target sensor log and each prior sensor log; selecting a sub-set of the prior sensor logs in dependence upon the statistical metrics; analysing the maintenance logs to correlate each prior sensor log included in the subset to one or more correlated maintenance tasks; selecting a

Abstract: Systems, methods, non-transitory computer readable media can be configured to accessing a target sensor log corresponding to a first machine; accessing one or more prior sensor logs corresponding to the first machine and one or more prior sensor logs corresponding to a plurality of second machines which are of the same type as the first machine; accessing a plurality of computer readable logs corresponding to the first machine and the second machines, the computer readable logs for each second machine comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type; determining a set of statistical metrics characterising a difference between the target sensor log and each prior sensor log; selecting a sub-set of the prior sensor logs in dependence upon the statistical metrics; analysing the maintenance logs to correlate each prior sensor log included in the subset to one or more correlated maintenance tasks; selecting a

Abstract: Systems, methods, non-transitory computer readable media can be configured to access a plurality of sensor logs corresponding to a first machine, each sensor log spanning at least a first period; access first computer readable logs corresponding to the first machine, each computer readable log spanning at least the first period, the computer readable logs comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type; determine a set of statistical metrics derived from the sensor logs; determine a set of log metrics derived from the computer readable logs; and determine, using a risk model that receives the statistical metrics and log metrics as inputs, fault probabilities or risk scores indicative of one or more fault types occurring in the first machine within a second period.

Abstract: Systems, methods, non-transitory computer readable media can be configured to access a plurality of sensor logs corresponding to a first machine, each sensor log spanning at least a first period. Access first computer readable logs corresponding to the first machine, each computer readable log spanning at least the first period, the computer readable logs comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type. Determine a set of statistical metrics derived from the sensor logs; determine a set of log metrics derived from the computer readable logs. Determine, using a risk model that receives the statistical metrics and log metrics as inputs, fault probabilities or risk scores indicative of one or more fault types occurring in the first machine within a second period.

Abstract: Systems, methods, non-transitory computer readable media can be configured to access a plurality of sensor logs corresponding to a first machine, each sensor log spanning at least a first period; access first computer readable logs corresponding to the first machine, each computer readable log spanning at least the first period, the computer readable logs comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type; determine a set of statistical metrics derived from the sensor logs; determine a set of log metrics derived from the computer readable logs; and determine, using a risk model that receives the statistical metrics and log metrics as inputs, fault probabilities or risk scores indicative of one or more fault types occurring in the first machine within a second period.

Abstract: Systems, methods, non-transitory computer readable media can be configured to accessing a target sensor log corresponding to a first machine; accessing one or more prior sensor logs corresponding to the first machine and one or more prior sensor logs corresponding to a plurality of second machines which are of the same type as the first machine; accessing a plurality of computer readable logs corresponding to the first machine and the second machines, the computer readable logs for each second machine comprising a maintenance log comprising a plurality of maintenance task objects, each maintenance task object comprising a time and a maintenance task type; determining a set of statistical metrics characterising a difference between the target sensor log and each prior sensor log; selecting a sub-set of the prior sensor logs in dependence upon the statistical metrics; analysing the maintenance logs to correlate each prior sensor log included in the subset to one or more correlated maintenance tasks; selecting a

Abstract: A system for providing software defined radio on a mobile device includes a hardware portion including an antenna and an interface configured to connect to the mobile device; a software portion configured for execution on the mobile device, the software portion configured to receive and process RF signals received at the antenna.

Abstract: A system for providing software defined radio on a mobile device includes a hardware portion including an antenna and an interface configured to connect to the mobile device; a software portion configured for execution on the mobile device, the software portion configured to receive and process RF signals received at the antenna.

Abstract: Disclosed are compounds, compositions and methods for treating diseases, syndromes, conditions and disorders that are affected by the inhibition of MGL, including pain. Such compounds are represented by Formula (I) as follows: wherein Y, r, R2 and Z are defined herein.

Abstract: Disclosed are compounds, compositions and methods for treating diseases, syndromes, conditions and disorders that are affected by the inhibition of MGL, including pain. Such compounds are represented by Formula (I) as follows: wherein R1, W and are defined herein.

Abstract: Disclosed are compounds, compositions and methods for treating diseases, syndromes, conditions and disorders that are affected by the inhibition of MGL, including pain. Such compounds are represented by Formula (I) as follows: wherein R1, W and are defined herein.

Abstract: Disclosed are compounds, compositions and methods for treating diseases, syndromes, conditions and disorders that are affected by the inhibition of MGL, including pain. Such compounds are represented by Formula (I) as follows: wherein Y, r, R2 and Z are defined herein.

Abstract: This invention relates to methods for preventing, treating or ameliorating an MMP9 and/or MMP13 mediated syndrome, disorder or disease comprising administering to a subject in need thereof an effective amount of a compound listed in the examples section of this specification, or a form, composition or medicament thereof. Disorders treated and/or prevented include rheumatoid arthritis.

Abstract: A method of screening a compound which modulates dipeptidyl peptidase I (DPPI) activities comprises the steps of adding a peptide substrate of DPPI to a reaction mixture which comprises DPPI and a compound, wherein the peptide substrate of DPPI has at least 3 amino acids and binds to a binding site of DPPI in addition to the S1-S2 site; and measuring the molecular weight of the substrate, wherein a change in the molecular weight of the substrate is indicative of the presence of DPPI activity.

Abstract: Disclosed are compounds, compositions and methods for treating diseases, syndromes, conditions and disorders that are affected by the inhibition of MGL, including pain. Such compounds are represented by Formula (I) as follows: wherein R1, W and are defined herein.