Stefano Barone

TL;DR: A statistically based engineering method, variation mode and effect analysis (VMEA), that facilitates an understanding of variation and highlights the product/process areas in which improvement efforts should be targeted is described.

TL;DR: This article presents a methodology for conducting a KE project in early development phases based on two new procedures based on calculating attribute importance weights by using respondent choice time in controlled interviews and an ordinal logistic regression model for analysing the results of CA experiments.

TL;DR: Extensions of the classical failure mode and effect analysis (FMEA) are presented and instead of technical calculation of probabilities from data that usually are too weak for correct results, statistical thinking is emphasized that puts the designers focus on the critical product functions.

TL;DR: It is demonstrated that ToBRFV was located in the seed coat, sometime in the endosperm, but never in the embryo; its transmission from infected seeds to plantlets occurs by micro-lesions during the germination.

TL;DR: A novel statistical system has been implemented for feature reduction and selection, while discriminant analysis was used as a method for feature classification, and it is believed that the model can be useful to improve treatment response and prognosis evaluation, potentially allowing the personalization of cancer treatment plans.