We discuss an alternative approach to quintessence modifying the usual equation of state of the cosmological fluid in order to see if going further than the approximation of perfect fluid allows to better reproduce the available data. We consider a cosmological model comprising only two fluids, namely baryons (modelled as dust) and dark matter with a Van der Waals equation of state. First, the general features of the model are presented and then the evolution of the energy density, the Hubble parameter and the scale factor are determined showing that it is possible to obtain accelerated expansion choosing suitably the model parameters. We use the estimated age of the universe and the data on the dimensionless coordinate distances to Type Ia supernovae and distant radio galaxies to see whether Van der Waals quintessence is viable to explain dark energy and to constrain its parameters.

In the present paper, we analyse ground tilt and seismicity at Campi Flegrei caldera and Ischia Island, two volcanic areas located in the south of Italy. These areas have been well studied for many years from a petrological, volcanological and geophysical view point. Moreover, due to the high seismic and volcanic risk for the populations living there, they are continuously monitored by networks of geophysical and geochemical sensors. We summarize the most important results that we obtained so far, concerning the observations of relationships between seismic activity and ground tilt anomalies, focusing on the time interval 2015–2018. First, we present a detailed description of the tiltmeter and seismic networks in both the investigated areas, as well as their development and improvement over time that has enabled high quality data collection. From the joint analysis of the seismic and borehole tiltmeter signals, we often notice concurrence between tilt pattern variations and the occurrence of seismicity. Moreover, the major tilt anomalies appear to be linked with the rate and energy of volcano-tectonic earthquakes, as well as with exogenous phenomena like solid Earth tides and hydrological cycles. The analysis that we present has potential applicability to other volcanic systems. Our findings show how the joint use tilt and seismic data can contribute to better understanding of the dynamics of volcanoes.

Long continuous seismic data recorded at five broadband seismic stations during 2006 at Campi Flegrei caldera have been analyzed. Introducing a coarse‐grained method, we evaluate the time evolution of amplitude and polarization of the seismic noise in the frequency band common to long‐period events. The series are modulated on tidal time scales: the root‐mean square is basically dominated by solar contribution, while the azimuth of the polarization vector shows lunar diurnal and semidiurnal constituents. In addition, we find that in the frequency band common to long‐period events the azimuths are polarized toward a specific area, suggesting that these persistent oscillations can be induced by the activity of the shallow geothermal reservoir.

Collecting massive seismic signals is a high-priority task in seismic risk evaluation, especially in densely populated areas, with cases of strong magnitude earthquake occurrence. At the same time, with the advent of the Internet of Things (IoT) paradigm, distributed and real-time environmental monitoring, supported by device interoperability, enhances the ability to collect data and make decisions especially in critical domains such as the seismic one. A crucial role is played by Semantic Web technologies that, in IoT ecosystems, promote syntactic and semantic interoperability, by enhancing the data quality that becomes ontology-annotated. This article introduces an IoT-oriented framework to collect seismic data, process and store them into a knowledge base. An ontology called Volcano Event Ontology (VEO) modeled for the seismic domain aims at gathering seismic signals collected by sensors for seismic event detection. The ontology is built on the well-known SSN/SOSA ontology, modeled to describe the systems of sensors, actuators, and observations. Seismic data have been collected by monitoring networks at Mt. Vesuvius (Naples, Italy) and Colima volcano (Mexico) and consolidated in the ontology. Moreover, the seismic data are also processed by a classification module to detect different seismic events (Volcano-Tectonic and long-period earthquakes, underwater explosions, and quarry blasts) and then stored in the knowledge base. Prompt detection and classification are, indeed, relevant to track any variation in the volcano dynamics, becoming crucial in cases of explosive crises. Finally, the VEO-driven knowledge base can be queried to get time-based seismic data and detected events, by queries.