Context. Gravitational waves from black-hole (BH) merging events have revealed a population of extra-galactic BHs residing in short-period binaries with masses that are higher than expected based on most stellar evolution models – and also higher than known stellar-origin black holes in our Galaxy. It has been proposed that those high-mass BHs are the remnants of massive metal-poor stars. Aims. Gaia astrometry is expected to uncover many Galactic wide-binary systems containing dormant BHs, which may not have been detected before. The study of this population will provide new information on the BH-mass distribution in binaries and shed light on their formation mechanisms and progenitors. Methods. As part of the validation efforts in preparation for the fourth Gaia data release (DR4), we analysed the preliminary astrometric binary solutions, obtained by the Gaia Non-Single Star pipeline, to verify their significance and to minimise false-detection rates in high-mass-function orbital solutions. Results. The astrometric binary solution of one source, Gaia BH3, implies the presence of a 32.70 ± 0.82 M_⊙ BH in a binary system with a period of 11.6 yr. Gaia radial velocities independently validate the astrometric orbit. Broad-band photometric and spectroscopic data show that the visible component is an old, very metal-poor giant of the Galactic halo, at a distance of 590 pc. Conclusions. The BH in the Gaia BH3 system is more massive than any other Galactic stellar-origin BH known thus far. The low metallicity of the star companion supports the scenario that metal-poor massive stars are progenitors of the high-mass BHs detected by gravitational-wave telescopes. The Galactic orbit of the system and its metallicity indicate that it might belong to the Sequoia halo substructure. Alternatively, and more plausibly, it could belong to the ED-2 stream, which likely originated from a globular cluster that had been disrupted by the Milky Way.

Mobility and ambient conditions are key factors in urban environments, affecting well-being and quality of life. In this context, sensors, smart mobility, networks, connectivity can play a significant and strategic role, being exploited with the aim of improving data and information available to public administration and to each citizen. In this way, they can be supported in having more sustainable and aware behaviours and in getting useful information and services, improving their daily activities. In this paper, we present a prototype of smart bike eco-system, designed with the aim of collecting, aggregating and sharing data about air pollution and about the urban environment, which can be exploited in a smart mobility context thanks to sensor and vehicular networks.

Studies and systems that are aimed at the identification of the presence of people within an indoor environment and the monitoring of their activities and flows have been receiving more attention in recent years, specifically since the beginning of the COVID-19 pandemic. This paper proposes an approach for people counting that is based on the use of cameras and Raspberry Pi platforms, together with an edge-based transfer learning framework that is enriched with specific image processing strategies, with the aim of this approach being adopted in different indoor environments without the need for tailored training phases. The system was deployed on a university campus, which was chosen as the case study. The proposed system was able to work in classrooms with different characteristics. This paper reports a proposed architecture that could make the system scalable and privacy compliant and the evaluation tests that were conducted in different types of classrooms, which demonstrate the feasibility of this approach. Overall, the system was able to count the number of people in classrooms with a maximum mean absolute error of 1.23.

Moving across a University campus (outdoor, among the buildings, and indoor, among classrooms and offices) could represent a barrier for students with disabilities, affecting their independence while they conduct their daily activities. Providing support by means of smart phones thanks to location technologies can be a useful means of integration and inclusion, with the effect of facilitating also tourists, newbies, or freshmen. This is particularly true in those contexts where the universities are hosted in historical buildings in old towns, which is a typical situation in the European countries. This paper presents AlmaWhere, a system based on beacon technology, designed and developed with the aim of equipping students of the University of Bologna with an indoor navigation system, providing them support in finding classes, labs, and libraries, with a specific attention those users with disabilities. The paper describes the main design issues, the system architecture, and technologies we have analyzed in order to design a prototype and two scenarios that involve personas.