Abstract: System, apparatus, user equipment, and associated computer program and computing methods are provided for facilitating efficient decision-making with respect to a subject entity. In one aspect, a labeled training dataset containing N records respectively corresponding to N entities is provided for training a decision engine based on performing supervised learning. Responsive to receiving a plurality of attribute values for the subject entity requiring a decision relative to an estimate of a performance variable based on at least a portion of the attribute values, the trained decision engine is configured to determine a credit risk score as a function obtained as a set of linearly decomposed constituent components corresponding to the attribute values of the subject entity, thereby effectuating an objective determination of which attributes contribute to what portions of the score in a computationally efficient manner.

Abstract: System, apparatus, user equipment, and associated computer program and computing methods are provided for facilitating efficient decision-making with respect to a subject entity. In one aspect, a labeled training dataset containing N records respectively corresponding to N entities is provided for training a decision engine based on performing supervised learning. Responsive to receiving a plurality of attribute values for the subject entity requiring a decision relative to an estimate of a performance variable based on at least a portion of the attribute values, the trained decision engine is configured to determine a decision score as a function obtained as a set of linearly decomposed constituent components corresponding to the attribute values of the subject entity, thereby effectuating an objective determination of which attributes contribute to what portions of the decision score in a computationally efficient manner.

Abstract: A method for simulating an ultrasonic response of a metal part is carried out by an electronic simulating device. The method includes computing a first distribution of ultrasonic waves for the part without defect, in response to an ultrasonic excitation toward said part computing a second distribution of ultrasonic waves for a predefined zone (S3k) of the part, including a defect (20), in response to an ultrasonic excitation toward said zone (S3k), with the computation of elementary distributions, each corresponding to an ultrasonic response received by a receiver located at a border (F) of said zone; and determining a resultant distribution of ultrasonic waves for the part with defect, from the first and second computed distributions, the resultant distribution forming a simulation of an ultrasonic response received from the part including the defect (20), in response to an ultrasonic excitation toward said part.

Abstract: A method for simulating an ultrasonic response of a metal part is carried out by an electronic simulating device. The method includes computing a first distribution of ultrasonic waves for the part without defect, in response to an ultrasonic excitation toward said part computing a second distribution of ultrasonic waves for a predefined zone (S3k) of the part, including a defect (20), in response to an ultrasonic excitation toward said zone (S3k), with the computation of elementary distributions, each corresponding to an ultrasonic response received by a receiver located at a border (F) of said zone; and determining a resultant distribution of ultrasonic waves for the part with defect, from the first and second computed distributions, the resultant distribution forming a simulation of an ultrasonic response received from the part including the defect (20), in response to an ultrasonic excitation toward said part.

Abstract: System, apparatus, user equipment, and associated computer program and computing methods are provided for facilitating efficient decision-making with respect to a subject entity. In one aspect, a labeled training dataset containing N records respectively corresponding to N entities is provided for training a decision engine based on performing supervised learning. Responsive to receiving a plurality of attribute values for the subject entity requiring a decision relative to an estimate of a performance variable based on at least a portion of the attribute values, the trained decision engine is configured to determine a decision score as a function obtained as a set of linearly decomposed constituent components corresponding to the attribute values of the subject entity, thereby effectuating an objective determination of which attributes contribute to what portions of the decision score in a computationally efficient manner.