The present disclosure relates to systems and methods for pharmaceutical injection supply management, and in particular, recommending an optimal amount of pharmaceutical injection supply for a user to load into a dispenser. Implementations of the systems and methods discussed herein can provide, via a mobile application, a recommendation to a pharmaceutical user of the amount of substance to load into their substance pump during a site change. The recommendation can be optimized to minimize substance costs and substance waste, or to accommodate a target replacement date and time.

A method of rating credit risk is provided. The method comprises calculating a number of credit risk factors associated with a financial instrument, wherein each credit risk factor is calculated iteratively at a first timestep as a discrete probabilistic wave function representing a superposition state of scores. The discrete probabilistic wave function of each credit risk factor is measured after each calculation iteration for the first timestep. The probabilistic wave functions of the credit risk factors are then linearly combined to calculate a discrete probabilistic wave function for a final credit rating of the financial instrument for the first timestep, which is displayed in a user interface. The above steps are repeated for a second timestep using the probabilistic wave functions of the credit risk factors at the first timestep as initial states for the second timestep.

A method of rating credit risk is provided. The method comprises calculating a number of credit risk factors associated with a financial instrument, wherein each credit risk factor is calculated iteratively at a first timestep as a discrete probabilistic wave function representing a superposition state of scores. The discrete probabilistic wave function of each credit risk factor is measured after each calculation iteration for the first timestep. The probabilistic wave functions of the credit risk factors are then linearly combined to calculate a discrete probabilistic wave function for a final credit rating of the financial instrument for the first timestep, which is displayed in a user interface. The above steps are repeated for a second timestep using the probabilistic wave functions of the credit risk factors at the first timestep as initial states for the second timestep.

A system and method for detecting anomalies in very large datasets is disclosed. The method includes calculating statistics for data elements in a data set over a range of time periods. These statistics are arranged into a 2D array and analyzed using a machine learning algorithm to detect anomalous regions. The method also includes steps of analyzing time series of the data based on detected anomalous regions, correcting any errors in the datasets, and storing the corrected values in a separate database to maintain data integrity.

A system and method for detecting anomalies in very large datasets is disclosed. The method includes calculating statistics for data elements in a data set over a range of time periods. These statistics are arranged into a 2D array and analyzed using a machine learning algorithm to detect anomalous regions. The method also includes steps of analyzing time series of the data based on detected anomalous regions, correcting any errors in the datasets, and storing the corrected values in a separate database to maintain data integrity.

A method for providing a training data set used for training a signal classification neural network is provided. The method includes generating at least one first virtual waveform primitive comprising a predetermined signal level and at least one second virtual waveform primitive comprising a signal edge. The training data set is formed and comprises a predetermined number of generated virtual waveform primitives including first virtual waveform primitives and second virtual waveform primitives. Each virtual waveform primitive comprises a sequence of time and amplitude discrete values. The training data set is used for training the signal classification neural network.

A method for providing a training data set used for training a signal classification neural network is provided. The method includes generating at least one first virtual waveform primitive comprising a predetermined signal level and at least one second virtual waveform primitive comprising a signal edge. The training data set is formed and comprises a predetermined number of generated virtual waveform primitives including first virtual waveform primitives and second virtual waveform primitives. Each virtual waveform primitive comprises a sequence of time and amplitude discrete values. The training data set is used for training the signal classification neural network.

A method of unsupervised learning of a metric representation and a corresponding system for a mobile device determines a metric position information for a mobile device from an environmental representation. The mobile device comprises at least one sensor for acquiring sensor data and an odometer system configured to acquire displacement data of the mobile device. An environmental representation is generated based on the acquired sensor data by applying an unsupervised learning algorithm. The mobile device moves along a trajectory and the displacement data and the sensor data are acquired while the mobile device is moving along the trajectory. A set of mapping parameters is calculated based on the environmental representation and the displacement data. A metric position estimation is determined based on a further environmental representation and the calculated set of mapping parameters.

A method of unsupervised learning of a metric representation and a corresponding system for a mobile device determines a metric position information for a mobile device from an environmental representation. The mobile device comprises at least one sensor for acquiring sensor data and an odometer system configured to acquire displacement data of the mobile device. An environmental representation is generated based on the acquired sensor data by applying an unsupervised learning algorithm. The mobile device moves along a trajectory and the displacement data and the sensor data are acquired while the mobile device is moving along the trajectory. A set of mapping parameters is calculated based on the environmental representation and the displacement data. A metric position estimation is determined based on a further environmental representation and the calculated set of mapping parameters.

A computer-implemented method of auditing drug supply chain data gathered from a distributed ledger is disclosed. The method includes receiving a population of drug product records from the distributed ledger. The method includes receiving a first set of drug product criteria. The method includes determining a weighted probability for one or more drug product records of the population of drug product records. The method includes generating a randomized first subset of drug product records from the population of drug product records based on the weighted probability of the one or more drug product records. Other methods, systems, and the like for unbiased drug selection for audit are also disclosed.