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Since the start of the COVID-19 pandemic, the top priority was to hinder the spread of the virus and prevent the collapse of the health systems. To achieve this, it was mandatory to ensure adequate supplies of personal protective equipment (PPE), which may not have been available in the short-term during the first months of the crisis. While leading manufacturing companies all over the world were retooling their factories to produce medical equipment, 3D printing offered a transition solution to quickly supply PPEs in the case of emergency. In this context, the University of Castilla-La Mancha started a new solidarity project to produce PPEs via 3D printing, especially for healthcare staff. This project was characterized by the commitment of several geographically distributed volunteers, with limitations due to confinement. The group of volunteers required an organizational model supported by ad-hoc technology to make their collaboration efficient. This article demonstrates how a centralized production model is not efficient in such a situation, and proposes a novel web system supported by an intelligent recommender, which facilitates a distributed organizational model that optimizes manufacturing and delivery times of PPEs. The results show how our proposal drastically improves the efficiency of the group of collaborators, and adequately address the high social demand concentrated in a short period. This was a crucial change for the production and distribution of over 23,500 PPEs during the first 40 days of the crisis, in one of the regions with a higher rate of COVID-19 cases in Europe.

The main goal of intelligent surveillance consists in providing security systems with the skills required for correctly detecting and analyzing events in monitored environments. Since these environments are complex and the information is distributed through them, the use of agent-based approaches has become more and more popular when monitoring moving objects. This paper describes how an existing agent platform has been adopted and used to give support to intelligent surveillance systems. Agents deployed by means of this platform implement a behavior-based model that is flexible enough to deal with the challenges that monitored environments pose. Two case studies of urban traffic environments are discussed to prove the feasibility of the proposal.