In the last decade, the availability of large mobility datasets such as Call Detail Records (CDR) [1, 2, 3], traces from GPS devices embedded in smartphones and cars [3], and geo-tagged posts on Location-Based Social Networks (LBSN) [4], allows characterizing human mobility from a statistical and mathematical point of view, uncovering the invisible rules that govern the individuals' displacements.
Soccer analytics is attracting increasing interest in academia and industry, thanks to the availability of sensing technologies that provide high-fidelity data streams for every match. Apart from a few sporadic attempts, it is only in recent years that soccer statistics have developed, thanks to sensing technologies that provide high-fidelity data streams extracted from every match. These data streams are mainly used by researchers in academia, data scientists in the industry, or sports data journalists to extract meaningful knowledge and tell stories.
The standard deviation of the inter-beats interval between QRS complexes recorded during 24 h (SDNN24) is considered the gold standard of Heart rate variability (HRV) features for cardiac health [1]. SDNN24 is an HRV feature that requires 24 h of continuous recording Inter-Beat Intervals, traditionally achieved using a Holter device, that makes the data collection difficult during people’s everyday life, therefore not performed routinely.
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Migration and integration issues are at the center of the political and public debate in Europe. The scale of international migration has increased further in recent years. According to the International Organization for Migration (IOM), the number of international migrants is estimated to be almost 272 million globally (3.5% of the world population).
Authors: Mirco Nanni, Agnese Bonavita, Riccardo Guidotti
Exploratory: Sustainable Cities for Citizens
The first days of 2021 were characterized by very serious episodes that “struck the latest and perhaps most savage blow to America's reputation as a paragon of liberal democracy” according to David Smith on The Guardian [1].
Traditional migration data sources such as census, survey, and register data have been the main sources of migration studies. However, data quality varies vastly from one country to another, making it difficult to establish consistency of data across different countries. This is because traditional data are costly and time-consuming; some countries cannot afford to collect such data [2].
A study born in Pisa has developed an algorithm capable of anticipating seasonal ailments two weeks earlier than previous models.
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