Abstract: An apparatus for correcting bunion deformity includes an elongated member, a first arm member extending from a first end to a second end, a second arm member, and a rotation guide. The first arm member has an arm axis and is coupled to the first end of the elongated member so that the arm axis extends in an orthogonal direction from the elongated member. The second arm member includes an attachment portion configured to translatably engage the elongated member and an extension extending in the same orthogonal direction as the first arm member. The rotation guide is coupled to the first arm member such that the rotation guide is configured to rotate with respect to the first arm member about a rotation axis, the rotation guide defining a bore having a longitudinal axis.

Abstract: Systems and methods for predicting crop diseases in a crop field using machine learning are disclosed. The method includes receiving one or more ambient weather parameters related to environmental conditions around the crop field from an environmental sensor; determining a disease risk indicator associated with the disease and the crop field based on the one or more ambient weather parameters, using a disease risk machine learning module trained on historical weather data from a weather database; receiving a pathogen incidence indicator related to the presence of a pathogen around the crop field from a pathogen sensor, wherein the pathogen is associated with the disease; and generating a disease score based on the disease risk indicator and the pathogen incidence indicator, where the disease score represents the severity of the disease around the crop field.

Abstract: Systems and methods for predicting crop diseases in a crop field using machine learning are disclosed. The method includes receiving one or more ambient weather parameters related to environmental conditions around the crop field from an environmental sensor; determining a disease risk indicator associated with the disease and the crop field based on the one or more ambient weather parameters, using a disease risk machine learning module trained on historical weather data from a weather database; receiving a pathogen incidence indicator related to the presence of a pathogen around the crop field from a pathogen sensor, wherein the pathogen is associated with the disease; and generating a disease score based on the disease risk indicator and the pathogen incidence indicator, where the disease score represents the severity of the disease around the crop field.