Temporal dynamics of benthic assemblages along a gradient of ocean acidification at a CO2 vent’s system of the Ischia Island

Global warming and ocean acidification, due to rising atmospheric levels of CO2, represent an actual threat to terrestrial and marine environments. Since Industrial Revolution, in less of 250 years, pH of surface seawater decreased on average of 0.1 unit, and is expected to further decreases of approximately 0.3-0.4 units by the end of this century. Naturally acidified marine areas, such as CO2 vent systems at the Ischia Island, allow to study acclimatation and adaptation of individual species as well as the structure of communities, and ecosystems to OA. The main aim of this thesis was to study how hard bottom sublittoral benthic assemblages changed trough time along a pH gradient. For this purpose, the temporal dynamics of mature assemblages established on artificial substrates (volcanic tiles) over a 3 year- period were analysed. Our results revealed how composition and dynamics of the community were altered and highly simplified at different level of seawater acidification. In fact, extreme low values of pH (approximately 6.9), affected strongly the assemblages, reducing diversity both in terms of taxa and functional groups, respect to lower acidification levels (mean pH 7.8) and ambient conditions (8.1 unit). Temporal variation was observed in terms of species composition but not in functional groups. Variability was related to species belonging to the same functional group, suggesting the occurrence of functional redundancy. Therefore, the analysis of functional groups kept information on the structure, but lost information on species diversity and dynamics. Decreasing in ocean pH is only one of many future global changes that will occur at the end of this century (increase of ocean temperature, sea level rise, eutrophication etc.). The interaction between these factors and OA could exacerbate the community and ecosystem effects showed by this thesis.

Analysis of the Variability of Carbonate System Parameters in the North-East Atlantic.

The purpose of this thesis is to analyse the spatial and temporal variability of the aragonite saturation state (ΩAR), commonly used as an indicator of ocean acidification, in the North-East Atlantic. When the aragonite saturation state decreases below a certain threshold, ΩAR <1, calcifying organisms (i.e. molluscs, pteropods, foraminifera, crabs, etc.) are subject to dissolution of shells and aragonite structures. This objective agrees with the challenge 'Ocean, climate change and acidification' of the EU COST Ocean Governance for Sustainability project, which aims to combine the information collected on the state of health of the oceans. Two open-sources data products, EMODnet and GLODAPv2, have been integrated and analysed for the first time in the North-East Atlantic region. The integrated dataset contains 1038 ΩAR vertical profiles whose time distribution spans from 1970 to 2014. The ΩAR has been computed from CO2SYS software considering different combinations of input parameters, pH, Total Alkalinity (TAlk) and Dissolved Inorganic Carbon (DIC), associated with Temperature, Salinity and Pressure at in situ conditions. A sensitivity analysis has been performed to better understand the data consistency of ΩAR computed from the different combinations of pH, Talk and DIC and to verify the difference among observed TAlk and DIC parameters and their output values from the CO2SYS tool. Maps of ΩAR have been computed with the best data coverage obtained from the two datasets, at different levels of depth in the area of investigation and they have been compared to the work of Jiang et al. (2015). The results are consistent and show similar horizontal and vertical patterns. The study highlights some aragonite undersaturated values (ΩAR <1) below 500 meters depth, suggesting a potential effect of acidification in the considered time period. This thesis aims to be a preliminary work for future studies that will be able to design the ΩAR variability on a decadal distribution based on the extended time-series acquired in this work.

Loading and damage modelling for an integrated SHM system of a prestressed concrete railway bridge in Naples

The thesis explores recent technology developments in the field of structural health monitoring and its application to railway bridge projects. It focuses on two main topics. First, service loads and effect of environmental actions are modelled. In particular, the train moving load and its interaction with rail track is considered with different degrees of detail. Hence, results are compared with real-time experimental measurements. Secondly, the work concerns the identification, definition and modelling process of damages for a prestressed concrete railway bridge, and their implementation inside FEM models. Along with a critical interpretation of the in-field measurements, this approach results in the development of undamaged and damaged databases for the AI-aided detection of anomalies and the definition of threshold levels to prompt automatic alert interventions. In conclusion, an innovative solution for the development of the railway weight-in-motion system is proposed.

Integrating an Emotion Recognition Model for the Flobi System

This thesis investigates if emotional states of users interacting with a virtual robot can be recognized reliably and if specific interaction strategy can change the users’ emotional state and affect users’ risk decision. For this investigation, the OpenFace [1] emotion recognition model was intended to be integrated into the Flobi [2] system, to allow the agent to be aware of the current emotional state of the user and to react appropriately. There was an open source ROS [3] bridge available online to integrate OpenFace to the Flobi simulation but it was not consistent with some other projects in Flobi distribution. Then due to technical reasons DeepFace was selected. In a human-agent interaction, the system is compared to a system without using emotion recognition. Evaluation could happen at different levels: evaluation of emotion recognition model, evaluation of the interaction strategy, and evaluation of effect of interaction on user decision. The results showed that the happy emotion induction was 58% and fear emotion induction 77% successful. Risk decision results show that: in happy induction after interaction 16.6% of participants switched to a lower risk decision and 75% of them did not change their decision and the remaining switched to a higher risk decision. In fear inducted participants 33.3% decreased risk 66.6 % did not change their decision The emotion recognition accuracy was and had bias to. The sensitivity and specificity is calculated for each emotion class. The emotion recognition model classifies happy emotions as neutral in most of the time.