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As the International Organization for Standardization (ISO) has stated in their original definition of quality (ISO 8402:1994), quality is “the totality of characteristics of an entity that bear on its ability to satisfy stated and implied needs.” The authors consequently believe that not only learning from challenges – the relevant entity – should be intended as a task aimed to satisfy the needs of both challenged learners and challenged businesses, but also that not all the entity's needs are stated from the beginning of the process.

These were the methodological assumptions of a workshop on destination management held from October 2020 to February 2021 in the frame of tourism studies at the University of Bergamo. Though the workshop was entirely run through digital channels in a time of pandemic, it successfully provided five destination management organizations (DMOs) and an association among hosts in the Bergamo Alps with a variety of digital communication products.

The workshop was special in terms of satisfaction expressed by participants as well as the involved DMOs. The participants' deliveries became active components of the destinations' policies. A professional video came as a welcome addition.

Implied needs which the workshop came across – namely, doubts on the reliability of tourism data, cooperation among local actors, prerequisites in building a new website, the role of food and recipes in promoting a destination identity, best practices in guiding guests through planned itineraries, and the role of a city administration in controlling overtourism – were identified while researching and producing.

The purpose of this paper is to present the Moore-Penrose pseudoinverse (PI) modeling and compare with artificial neural network (ANN) modeling for switched reluctance machine (SRM) performance.

In a design of an SRM, there are a number of parameters that are chosen empirically inside a certain interval, therefore, to find an optimal geometry it is necessary to define a good model for SRM. The proposed modeling uses the Moore-Penrose PI for the resolution of linear systems and finite element simulation data. To attest to the quality of PI modeling, a model using ANN is established and the two models are compared with the values determined by simulations of finite elements.

The proposed PI model showed better accuracy, generalization capacity and lower computational cost than the ANN model.

The proposed approach can be applied to any problem as long as experimental/computational results can be obtained and will deliver the best approximation model to the available data set.