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Pervasive healthcare is an emerging research discipline, which focuses on the development of pervasive and ubiquitous computing technology for healthcare environments. Information and Communication Technologies have dramatically evolved during the last decade, laying a solid foundation for the future generation of Ubiquitous Internet access. As a result, current efforts in research and development in the areas of pervasive healthcare, promote the formation of inter-disciplinary international teams of experts, scientists, researchers and engineers to create a new generation of applications and technologies that will facilitate the fully automated information cyberspace systems. The authors discuss the current state-of-the-art in the world of Telecommunications and Internet Technologies as well as new technological trends in the Internet and Automation Industries, while promoting research and development in the interdisciplinary projects conducted by multinational teams worldwide.

Abstract: We apply and extend the priority algorithm framework introduced by Borodin, Nielsen, and Rackoff to define “greedy-like” algorithms for the (uncapacitated) facility location problems and set cover problems. These problems have been the focus of extensive research from the point of view of approximation algorithms and for both problems greedy-like algorithms have been proposed and analyzed. The priority algorithm definitions are general enough to capture a broad class of algorithms that can be characterized as “greedy-like” while still possible to derive non-trivial lower bounds on the approximability of the problems by algorithms in such a class. Our results are orthogonal to complexity considerations, and hence apply to algorithms that are not necessarily polynomial time.

We explore whether training protocols can enhance the ability of social media users to detect fake news, by conducting an online experiment ( N  = 417) to analyse the effect of such a training protocol, while considering the role of scepticism, age, and level of education. Our findings show a significant relationship between the training protocol and the ability of social media users to detect fake news, suggesting that the protocol can play a positive role in training social media users to recognize fake news. Moreover, we find a direct positive relationship between age and level of education on the one hand and ability to detect fake news on the other, which has implications for future research. We demonstrate the potential of training protocols in countering the effects of fake news, as a scalable solution that empowers users and addresses concerns about the time-consuming nature of fact-checking., Competing Interests: Conflict of InterestAll the named authors have contributed substantially to conducting the underlying research and preparing the manuscript, and none of them has any conflicts of interest, financial or otherwise., (© The Author(s) 2022.)