Underwater noise pollution from shipping activities is widely recognised as a significant threat to marine life. Noise emitted by vessels can have various detrimental effects on fish and marine ecosystems. Accurately estimating and analysing vessel-generated underwater noise is therefore of critical importance for the protection and conservation of marine environments. In this paper, we present an enhanced version of our model for the spatiotemporal characterisation of vessel-generated underwater noise, with a focus on improving its scalability. The original model was limited to fishing vessels and relied on Automatic Identification System (AIS) data to reconstruct trajectories, as well as engine horsepower to estimate emitted noise. Here, we generalise the approach to include all vessel categories — including tankers, cruise ships, and recreational boats — still relying on AIS data, but estimating noise as a function of vessel length overall (LOA) and category, since horsepower information is not available for all vessels in the dataset. We broaden the study area to include the Central Adriatic Sea, in addition to the Northern part previously considered. The enlarged area and the substantially greater volume of AIS data introduce significant computational challenges, making scalability a primary concern. We address these challenges through a comprehensive analysis of optimisation strategies to improve query execution performance. In particular, we restructure the computational pipeline by implementing table partitioning and leveraging parallelisation techniques. Specifically, we employ PostgreSQL’s native parallel query execution and implement multiple partitioning strategies, including range, hash, and list partitioning. We further explore spatial partitioning through space tiling, comparing regular, adaptive, and k-d tree-based grids. Finally, we leverage the Citus extension to distribute computation across four and eight nodes. Our approach improves computational efficiency while preserving the accuracy of noise calculation, offering a scalable solution for large datasets.

Underwater noise pollution from human activities, particularly shipping, has been recognised as a serious threat to marine life. The sound generated by vessels can have various adverse effects on fish and aquatic ecosystems in general. In this setting, the estimation and analysis of the underwater noise produced by vessels is an important challenge for the preservation of the marine environment. In this paper we propose a model for the spatiotemporal characterisation of the underwater noise generated by vessels. The approach is based on the reconstruction of the vessels’ trajectories from Automatic Identification System (AIS) data and on their deployment in a spatiotemporal database. Trajectories are enriched with semantic information like the acoustic characteristics of the vessels’ engines or the activity performed by the vessels. We define a model for underwater noise propagation and use the trajectories’ information to infer how noise propagates in the area of interest. We develop our approach for the case study of the fishery activities in the Northern Adriatic Sea, an area of the Mediterranean Sea which is well known to be highly exploited. We implement our approach using MobilityDB, an open source geospatial trajectory data management and analysis platform, which offers spatiotemporal operators and indices improving the efficiency of our system. We use this platform to conduct various analyses of the underwater noise generated in the Northern Adriatic Sea, aiming at estimating the impact of fishing activities on underwater noise pollution and at demonstrating the flexibility and expressiveness of our approach.

Nowadays, physicians have at their hands a huge amount of data produced by a large set of diagnostic and instrumental tests integrated with data obtained by high-throughput technologies. If such data were opportunely linked and analysed, they might be used to strengthen predictions, so that to improve the prevention and the time-to-diagnosis, reduce the costs of the health system, and bring out hidden knowledge. Machine learning is the principal technique used nowadays to leverage data and gain useful information. However, it has led to various challenges, such as improving the interpretability and explainability of the employed predictive models and integrating expert knowledge into the final system. Solving those challenges is of paramount importance to enhance the trust of both clinicians and patients in the system predictions. To solve the aforementioned issues, in this paper we propose a software workflow able to cope with the trustworthiness aspects of machine learning models and considering a multitude of heterogeneous data and models.

BACKGROUND: Residue Interaction Networks (RINs) map the crystallographic description of a protein into a graph, where amino acids are represented as nodes and non-covalent bonds as edges. Determination and visualization of a protein as a RIN provides insights on the topological properties (and hence their related biological functions) of large proteins without dealing with the full complexity of the three-dimensional description, and hence it represents an invaluable tool of modern bioinformatics. RESULTS: We present RINmaker, a fast, flexible, and powerful tool for determining and visualizing RINs that include all standard non-covalent interactions. RINmaker is offered as a cross-platform and open source software that can be used either as a command-line tool or through a web application or a web API service. We benchmark its efficiency against the main alternatives and provide explicit tests to show its performance and its correctness. CONCLUSIONS: RINmaker is designed to be fully customizable, from a simple and handy support for experimental research to a sophisticated computational tool that can be embedded into a large computational pipeline. Hence, it paves the way to bridge the gap between data-driven/machine learning approaches and numerical simulations of simple, physically motivated, models.

Abstract In this paper we model spatio-temporal data describing the fishing activities in the Northern Adriatic Sea over four years. We build, implement and analyze a database based on the fusion of two complementary data sources: trajectories from fishing vessels (obtained from terrestrial Automatic Identification System, or AIS, data feed) and fish catch reports (i.e., the quantity and type of fish caught) of the main fishing market of the area. We present all the phases of the database creation, starting from the raw data and proceeding through data exploration, data cleaning, trajectory reconstruction and semantic enrichment. We implement the database by using MobilityDB, an open source geospatial trajectory data management and analysis platform. Subsequently, we perform various analyses on the resulting spatio-temporal database, with the goal of mapping the fishing activities on some key species, highlighting all the interesting information and inferring new knowledge that will be useful for fishery management. Furthermore, we investigate the use of machine learning methods for predicting the Catch Per Unit Effort (CPUE), an indicator of the fishing resources exploitation in order to drive specific policy design. A variety of prediction methods, taking as input the data in the database and environmental factors such as sea temperature, waves height and Clorophill-a, are put at work in order to assess their prediction ability in this field. To the best of our knowledge, our work represents the first attempt to integrate fishing ships trajectories derived from AIS data, environmental data and catch data for spatio-temporal prediction of CPUE – a challenging task.

We consider trophic networks, a kind of networks used in ecology to represent feeding interactions (what-eats-what) in an ecosystem. Starting from the observation that trophic networks can be naturally modelled as Petri nets, we explore the possibility of using Petri nets for the analysis and simulation of trophic networks. We define and discuss different continuous Petri net models, whose level of accuracy depends on the information available for the modelled trophic network. The simplest Petri net model we construct just relies on the topology of the network. We also propose a technique for deriving a more refined model that embeds into the Petri net the known constraints on the transition rates that represent the knowledge on metabolism and diet of the species in the network. Finally, if the information of the biomass amounts for each species at steady state is available, we discuss a way of further refining the Petri net model in order to represent dynamic behaviour. We apply our Petri net technology to a case study of the Venice lagoon and analyse the results.

Over the last decade, a lot of research has been directed toward integrating performance analysis into the software development process. Traditional software development methods focus on software correctness, introducing performance issues later in the development process. This approach does not take into account the fact that performance problems may require considerable changes in design, for example, at the software architecture level, or even worse at the requirement analysis level. Several approaches were proposed in order to address early software performance analysis. Although some of them have been successfully applied, we are still far from seeing performance analysis integrated into ordinary software development. In this paper, we present a comprehensive review of recent research in the field of model-based performance prediction at software development time in order to assess the maturity of the field and point out promising research directions.