Scientific Publications

Abstract
The high number of offshore platforms at the end of their productive phase offers the opportunity of their re-use and the development of effective management solutions, such as the possibility of utilizing them as ecological observatories for monitoring marine ecosystems and their biological resources. Here, through a multiparametric observatory deployed at an unproductive offshore platform, located in the Central Adriatic Sea (Mediterranean Sea), we collected data for 13 months on benthopelagic fish assemblage and habitat conditions. A total of 155.5 h of high-frequency (30 min) video-monitoring, recorded higher fish abundances during spring-summer periods during daytime, while fish diversity was highest in autumn. Some environmental variables contributed significantly to explain the overall community variance. Our results suggest that offshore platforms can be re-converted into ecological observatories, to collect relevant amounts of information that can be difficulty obtained with alternative approaches, contributing to our understanding of changes occurring in open water ecosystems.

Abstract
This review examines the integration of blockchain technology with the IoT in the Marine Internet of Things (MIoT) and Internet of Underwater Things (IoUT), with applications in areas such as oceanographic monitoring and naval defense. These environments present distinct challenges, including a limited communication bandwidth, energy constraints, and secure data handling needs. Enhancing BIoT systems requires a strategic selection of computing paradigms, such as edge and fog computing, and lightweight nodes to reduce latency and improve data processing in resource-limited settings. While a blockchain can improve data integrity and security, it can also introduce complexities, including interoperability issues, high energy consumption, standardization challenges, and costly transitions from legacy systems. The solutions reviewed here include lightweight consensus mechanisms to reduce computational demands. They also utilize established platforms, such as Ethereum and Hyperledger, or custom blockchains designed to meet marine-specific requirements. Additional approaches incorporate technologies such as fog and edge layers, software-defined networking (SDN), the InterPlanetary File System (IPFS) for decentralized storage, and AI-enhanced security measures, all adapted to each application’s needs. Future research will need to prioritize scalability, energy efficiency, and interoperability for effective BIoT deployment.

Abstract
Interest in mesopelagic fish layers is on the rise due to the potential exploitability of their macrofauna; hence, profiling their fish community is crucial to enable the sustainability of future fishing practices. In this context, a dedicated survey was carried out in September 2022 along the Irish shelf break, where fishing (catch) and eDNA metabarcoding analysis using a portable high-throughput sequencer were performed to investigate the fish community of the upper mesopelagic layer. Catch data showed that the targeted layers consisted mainly of the young-of-the-year cohort of Mueller’s pearlside (Maurolicus muelleri), a mesopelagic fish, with little bycatch. eDNA data reflected the high prevalence of M. muelleri’s eDNA (56%–97% of assigned reads), identified species undetected by fishing, and showed that the least represented species differed in water samples collected before or after fishing activities. While this reflects current limitations of each technique, it also shows that a multidisciplinary approach may provide an increased level of resolution for M. muelleri layer’s ancillary fish community. Findings from the present study provided important insights to further refine sample acquisition and rapid processing of eDNA metabarcoding data, which beholds great potential to corroborate fishing methods when ground truthing acoustic approaches in mesopelagic fish layers assessments.

Abstract
Human activity depends on the oceans for food, transportation, leisure, and many more purposes. Oceans cover 70% of the Earth’s surface, but most of them are unknown to humankind. This is the reason why underwater imaging is a valuable resource asset to Marine Science. Images are acquired with observing systems, e.g. autonomous underwater vehicles or underwater observatories, that presently transmit all the raw data to land stations. However, the transfer of such an amount of data could be challenging, considering the limited power supply and transmission bandwidth of these systems. In this paper, we discuss these aspects, and in particular how it is possible to couple Edge and Cloud computing for effective management of the full processing pipeline according to the Compute Continuum paradigm.

Abstract
Monitoring the effects of climate change and other multi-years processes on coastal ecosystems require long-term datasets that may extend into decades. One tool to achieve this are cabled seafloor observatories that can collect continual streams of environmental and biological data as long as the equipment is maintained. Here, we used 10-years of time-lapse images (every 30 mins) from the OBSEA seafloor cabled observatory located at 20 m depth, four km offshore from Vilanova i la Geltrú (Spain) coast, to characterize temporal trends in fish community dynamics. These temporal trends were compared to in situ and remotely-sensed (MODIS-Aqua) data on temperature, salinity, and chlorophyll-a concentration (Chl-a). We observed a reduction in fish diversity over time and an increase in species turnover. Specifically, there was a decrease in the relative abundance of fish species at the lowest trophic levels alongside an increase in predators, suggesting a top-down effect. Of temperature, salinity, and Chl-a, only salinity exhibited a significant change over time. Nevertheless, the Generalized Additive Models (GAMs) revealed significant correlations between fish biodiversity indices and both temperature and Chl-a. Following models results we concluded that environmental variables affected the local fish community only at seasonal level. Including more environmental variables, such as fishing activity and pollution, in the applied models may help explain the detected decreases in biodiversity.

Abstract
Artificial Intelligence (AI) applications related to agriculture have recently gained in use and attention. They are indeed valuable tools for interpreting data, improving production chains, and optimizing the use of natural resources. Among AI models, the most recent and promising area is represented by Generative Artificial Intelligence (GAI). After an initial description of its general model architectures, this work aims to review its practical uses and potentials in the following individual sectors: agriculture, precision farming, and animal farming, as well as interdisciplinary applications. The literature search was carried out using the SCOPUS, Google Scholar, and Web of Science databases. GAI holds immense potential for revolutionizing agriculture, offering solutions ranging from precision farming to pest management and supply chain optimization. Though some applications can extend beyond efficiency gains, and hallucinations occurrence i.e. false output information presented as fact, remains an open issue, GAI can be decisive for tasks like improving training datasets, refining models, and facilitating time series analysis. This review extensively describes the vital importance of these tasks for agriculture, precision and animal farming, caused by the rise of new technologies. As a result, by embracing and responsibly implementing GAI applications, it is possible to create a more sustainable and resilient future for agriculture and precision farming. GAI have the capacity to extract specific information from big data systems, offering huge potential to meet a growing global population demand and consequent environmental challenges for the future.

Abstract
Edge computing on mobile marine platform is paramount for automated ecological monitoring. The goal of demonstrating the computational feasibility of an Artificial Intelligence (AI)-powered camera for fully automated real-time species-classification on deep-sea crawler platforms was searched by running You-Only-Look-Once (YOLO) model on an edge computing device (NVIDIA Jetson Nano), to evaluate the achievable animal detection performances, execution time and power consumption, using all the available cores. We processed a total of 337 rotating video scans (∼180°), taken during approximately 4 months in 2022 at the methane hydrates site of Barkley Canyon (Vancouver Island; BC; Canada), focusing on three abundant species (i.e., Sablefish Anoplopoma fimbria, Hagfish Eptatretus stoutii, and Rockfish Sebastes spp.). The model was trained on 1926 manually annotated video frames and showed high detection test performances in terms of accuracy (0.98), precision (0.98), and recall (0.99). The trained model was then applied on 337 videos.
In 288 videos we detected a total of 133 Sablefish, 31 Hagfish, and 321 Rockfish nearly in real-time (about 0.31 s/image) with very low power consumption (0.34 J/image). Our results have broad implications on intelligent ecological monitoring. Indeed, YOLO model can meet operational-autonomy criteria for fast image processing with limited computational and energy loads.

Abstract
Cabled multiparametric observatories are sustaining ecological monitoring by collecting long-term real-time biological and environmental data. Here, we investigated fish communities by sampling environmental DNA (eDNA) over 4 days near the multiparametric cabled video-observatory OBSEA (Northwestern Mediterranean Sea). The multi-marker eDNA metabarcoding approach resulted in an increased species detection and helped provide a more comprehensive view of the local fish community when combined with imaging data. These results underline the potential of omics methods in long-term monitoring of economically and ecologically important fish species.

Abstract
This study evaluates leadership uniformity—the degree to which the proposer role is evenly distributed among validator nodes over time—in Quorum-based Byzantine Fault Tolerance (QBFT), a Byzantine Fault-Tolerant (BFT) consensus algorithm used in permissioned blockchain networks. By introducing simulated follower timeouts derived from uniform, normal, lognormal, and Weibull distributions, it models a range of network conditions and latency patterns across nodes. This approach integrates Raft-inspired timeout mechanisms into the QBFT framework, enabling a more detailed analysis of leader selection under different network conditions. Three leader selection strategies are tested: Direct selection of the node with the shortest timeout, and two quorum-based approaches selecting from the top 20% and 30% of nodes with the shortest timeouts. Simulations were conducted over 200 rounds in a 10-node network. Results show that leader selection was most equitable under the Weibull distribution with shape 𝑘=0.5
, which captures delay behavior observed in real-world networks. In contrast, the uniform distribution did not consistently yield the most balanced outcomes. The findings also highlight the effectiveness of quorum-based selection: While choosing the node with the lowest timeout ensures responsiveness in each round, it does not guarantee uniform leadership over time. In low-variability distributions, certain nodes may be repeatedly selected by chance, as similar timeout values increase the likelihood of the same nodes appearing among the fastest. Incorporating controlled randomness through quorum-based voting improves rotation consistency and promotes fairer leader distribution, especially under heavy-tailed latency conditions. However, expanding the candidate pool beyond 30% (e.g., to 40% or 50%) introduced vote fragmentation, which complicated quorum formation in small networks and led to consensus failure. Overall, the study demonstrates the potential of timeout-aware, quorum-based leader selection as a more adaptive and equitable alternative to round-robin approaches, and provides a foundation for developing more sophisticated QBFT variants tailored to latency-sensitive networks.

Abstract
Effective marine conservation and management require ecological monitoring in the form of intensive real-time data collection over large spatial scales. The combined use of fixed platforms (e.g., cabled observatories) and research vessels with platforms of different levels of teleoperated autonomy (e.g., remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs) can contribute to the acquisition of large multiparametric biological and environmental data. If those data are spatially combined, sufficient spatial coverage can be achieved for ecological monitoring. A digital twin of the ocean (DTO) approach can then be used as a virtual representation of that monitored space, enabling multiparametric analyses of environmental patterns and processes affecting biodiversity and species distributions, as well as socioeconomic activities. Here, we propose a general architecture for a DTO centred on real-time data collection from local networks on fixed and mobile platforms, such as the physical twin observers (PTO), which is synergistically merged with platforms operating at large geographic scales. We describe a roadmap to achieve this DTO via 4 key steps: (1) acquisition of in situ data with a robotic network of platforms; (2) the application of AI in image processing for extracting biological data; (3) big data management with data bubbles; and (4) development of the resulting DTO framework for providing ecosystem monitoring via the computation of ecological indicators and socioecological modelling.