Alberto Trombetta

Computer power is a constantly increasing demand in scientific data analyses, in particular when Markov Chain Monte Carlo (MCMC) methods are involved, for example for estimating integral functions or Bayesian posterior probabilities. In this paper, we describe the benefits of a parallel computation of MCMC using a cloud-based, serverless architecture: first, the computation time can be spread over thousands of processes, hence greatly reducing the time the user should wait to have its computation completed. Second, the overhead time required for running in parallel several processes is minor and grows logarithmically with respect to the number of processes. Third, the serverless approach does not require time-consuming efforts for maintaining and updating the computing infrastructure when/if the number of walkers increases or for adapting the code to optimally use the infrastructure. The benefits are illustrated with the computation of the posterior probability distribution of a real astronomical analysis.

The increase in older workers in industrialized countries has become evident in the past two decades. The need to support the ageing workforce to effectively perform their tasks has resulted in Ambient Assisted Working (AAW), consisting of developing “smart” systems that can adapt themselves to workers’ needs by exploiting ambient intelligence (AmI) solutions. In AAW, AmI provides flexible workplace adaptations for a wide range of older workers (including persons characterized by chronic conditions and disabilities), while ensuring the ageing workforce’s safety and comfort within the workplace. This work proposes a systematic literature review with the aim of identifying trends among existing AAW solutions specifically designed for older workers. The review adopted the PRISMA methodology, focusing on journal articles and surveying more than 1500 works. The review underlined an absence of articles completely devoted to this research question. Nonetheless, by extending the research question to existing AmI solutions for workers that could potentially be able to support older workers in performing their working activities, it was possible to draw some considerations on the adoption of AmI for the ageing workforce. Among them, the review identified the different types of supporting AmI solutions provided to AAW, which technologies were adopted, and which workplaces were investigated the most. Finally, this work leveraged the findings of the review process to sketch some future research directions for AAW as a discipline.