Abstract: Systems and methods are disclosed for training a model to predict a cybersecurity risk based on entity firmographics. A breach dataset comprising a number of breach indicator values for a number of entities is generated, wherein each respective breach indicator value is (i) mapped to a respective entity of the entities and (ii) an evaluation of at least one of the first security incidents being associated with the respective entity during a time period. A number of aggregated risk feature values for a plurality of geographic locations are determined based on a plurality of second security observations. The aggregated risk feature values are joined to the breach indicator values and firmographic parameter values to form a training dataset. A model is trained using the training dataset to generate a predictive risk assessment for an entity of the entities based on the firmographic parameter values associated with the entity.

Abstract: Systems and methods for use in identifying a set of candidate seeds for a target field based on a prediction model are provided. One example method includes accessing, by a computing device, data from a data server, the data including data representative of seeds harvested from at least one of a research growing space, a development growing space, and a field growing space; generating a yield delta prediction model, based on at least a portion of the accessed data; for each of a plurality of candidate seeds, automatically generating a probability of a yield delta for the candidate seed, relative to a target seed, exceeding a performance threshold, based on the generated model; identifying, by the computing device, a set of the candidate seeds, based on the probability of the respective candidate seed satisfying a defined threshold; and outputting, by the computing device, the identified set of seeds to a user.

Abstract: Systems and methods for utilizing a spatial statistical model to maximize efficacy in performing trials on agronomic fields are disclosed herein. In an embodiment, a system receives first yield data for a first portion of an agronomic field having received a first treatment, and second yield data for a second portion of the agronomic field having received a second treatment different than the first treatment. The system uses a spatial statistical model and the first yield data to compute a yield value for the second portion of the agronomic field, where the yield value indicates an agronomic yield for the second portion of the agronomic field if the second portion of the agronomic field had received the first treatment instead of the second treatment. Based on the computed yield value and the second yield data, the system selects the second treatment and generates a prescription map including the second treatment.

Abstract: Systems and methods for utilizing a spatial statistical model to maximize efficacy in performing trials on agronomic fields are disclosed herein. In an embodiment, a system receives first yield data for a first portion of an agronomic field, the first portion of the agronomic field having received a first treatment, and second yield data, for a second portion of the agronomic field, the second portion of the agronomic field having received a second treatment that is different than the first treatment. The system uses a spatial statistical model and the first yield data to compute a yield value for the second portion of the agronomic field, the yield value indicating an agronomic yield for the second portion of the agronomic field if the second portion of the agronomic field had received the first treatment instead of the second treatment. Based on the computed yield value and the second yield data, the system selects the second treatment.

Abstract: Systems and methods for utilizing a spatial statistical model to maximize efficacy in performing trials on agronomic fields are disclosed herein. In an embodiment, a system receives first yield data for a first portion of an agronomic field having received a first treatment, and second yield data for a second portion of the agronomic field having received a second treatment different than the first treatment. The system uses a spatial statistical model and the first yield data to compute a yield value for the second portion of the agronomic field, where the yield value indicates an agronomic yield for the second portion of the agronomic field if the second portion of the agronomic field had received the first treatment instead of the second treatment. Based on the computed yield value and the second yield data, the system selects the second treatment and generates a prescription map including the second treatment.

Abstract: Systems and methods for utilizing a spatial statistical model to maximize efficacy in performing trials on agronomic fields are disclosed herein. In an embodiment, a system receives first yield data for a first portion of an agronomic field, the first portion of the agronomic field having received a first treatment, and second yield data, for a second portion of the agronomic field, the second portion of the agronomic field having received a second treatment that is different than the first treatment. The system uses a spatial statistical model and the first yield data to compute a yield value for the second portion of the agronomic field, the yield value indicating an agronomic yield for the second portion of the agronomic field if the second portion of the agronomic field had received the first treatment instead of the second treatment. Based on the computed yield value and the second yield data, the system selects the second treatment.

Abstract: Systems and methods for use in identifying a set of candidate seeds for a target field based on a prediction model are provided. One example method includes accessing, by a computing device, data from a data server, the data including data representative of seeds harvested from at least one of a research growing space, a development growing space, and a field growing space; generating a yield delta prediction model, based on at least a portion of the accessed data; for each of a plurality of candidate seeds, automatically generating a probability of a yield delta for the candidate seed, relative to a target seed, exceeding a performance threshold, based on the generated model; identifying, by the computing device, a set of the candidate seeds, based on the probability of the respective candidate seed satisfying a defined threshold; and outputting, by the computing device, the identified set of seeds to a user.

Abstract: Systems and methods for utilizing a spatial statistical model to maximize efficacy in performing trials on agronomic fields are disclosed herein. In an embodiment, a system receives first yield data for a first portion of an agronomic field, the first portion of the agronomic field having received a first treatment, and second yield data, for a second portion of the agronomic field, the second portion of the agronomic field having received a second treatment that is different than the first treatment. The system uses a spatial statistical model and the first yield data to compute a yield value for the second portion of the agronomic field, the yield value indicating an agronomic yield for the second portion of the agronomic field if the second portion of the agronomic field had received the first treatment instead of the second treatment. Based on the computed yield value and the second yield data, the system selects the second treatment.

Abstract: Systems and methods for utilizing a spatial statistical model to maximize efficacy in performing trials on agronomic fields are disclosed herein. In an embodiment, a system receives first yield data for a first portion of an agronomic field, the first portion of the agronomic field having received a first treatment, and second yield data, for a second portion of the agronomic field, the second portion of the agronomic field having received a second treatment that is different than the first treatment. The system uses a spatial statistical model and the first yield data to compute a yield value for the second portion of the agronomic field, the yield value indicating an agronomic yield for the second portion of the agronomic field if the second portion of the agronomic field had received the first treatment instead of the second treatment. Based on the computed yield value and the second yield data, the system selects the second treatment.

Abstract: Disclosed is a method and solvent composition capable of removing iron oxide deposits from the surface of titanium components without substantially damaging the underlying titanium component. Iron oxide deposits may be removed from the surface of a titanium component by contacting the titanium component with the solvent composition of the invention. The solvent composition may then be removed from contact with the titanium component to obtain a recyclable solvent composition which is recycled into repeated contact with the titanium component. The solvent composition comprises an aqueous mixture of an organic acid and a hydrohalide acid.

Abstract: Protective package for an elongated medical device, comprises a protective sheath including a lumen sized to receive a body of the elongated medical device, wherein a first end of the sheath is adapted to receive a distal end of the elongated medical device and a second end of the sheath is adapted to receive a proximal end of the elongated medical device and a hydration opening disposed between the first and second ends of the sheath.