Abstract: A soil carbon sensor includes a probe body for placement into soil, a first detector supported by the probe body that transmits a frequency-modulated signal into the soil. The first detector includes a first electrode configured responsive to a moisture content and bulk density of the soil. A second detector is supported by the probe body and transmits an amplitude-modulated signal into the soil. The second detector includes a second electrode configured responsive to a soil organic carbon content of the soil. A processor is arranged in signal communication with the first and second detectors, the processor configured to generate and control the transmission of the frequency and amplitude modulated signals to monitor the first electrode to capture an impedance measurement indicative of the moisture content and bulk density, and to monitor the second electrode to capture a current measurement indicative of the soil organic carbon (SOC) content.

Abstract: A soil carbon sensor includes a probe body for placement into soil, a first detector supported by the probe body that transmits a frequency-modulated signal into the soil. The first detector includes a first electrode configured responsive to a moisture content and bulk density of the soil. A second detector is supported by the probe body and transmits an amplitude-modulated signal into the soil. The second detector includes a second electrode configured responsive to a soil organic carbon content of the soil. A processor is arranged in signal communication with the first and second detectors, the processor configured to generate and control the transmission of the frequency and amplitude modulated signals, to monitor the first electrode to capture an impedance measurement indicative of the moisture content and bulk density, and to monitor the second electrode to capture a current measurement indicative of the soil organic carbon (SOC) content.

Abstract: A system and method is described for providing custom predictive models for detecting electronic security threats within an enterprise computer network. The custom models may be defined in a declarative language. The custom models, along with native models, may be combined together to provide custom machine learning (ML) use cases.

Abstract: Sensitive data may be anonymized for use in user interfaces by applying a cryptographic hash function to the data. The hashed value may be broken into hash tokens and the hash tokens converted to human readable tokens using a 1:1 conversion function. The human readable tokens can then be concatenated together to provide a human readable identifier of the sensitive data.

Abstract: A system and method is described for providing custom predictive models for detecting electronic security threats within an enterprise computer network. The custom models may be defined in a declarative language. The custom models, along with native models, may be combined together to provide custom machine learning (ML) use cases.

Abstract: An object tracking system comprising at least one object to be tracked, a tracking device, a communication means, a central network system and a user operated device. Each of the objects to be tracked have location data detection means and is connected via the connection means with a communication network. The central network system is connected with the communication network and comprises an input means to receive the location data from the at least one object via the communication network, a relational database, a software engine and an output means to transmit data via the communication network. The at least one user operated display is connected to the communication network and is configured to display information relating to the data transmitted from the central network system.

Abstract: Sensitive data may be anonymized for use in user interfaces by applying a cryptographic hash function to the data. The hashed value may be broken into hash tokens and the hash tokens converted to human readable tokens using a 1:1 conversion function. The human readable tokens can then be concatenated together to provide a human readable identifier of the sensitive data.