arXiv subjects

Orazio Giustolisi

Publications and source records attributed to Orazio Giustolisi.

3 recordsLinked to original sources

arXiv2025

A novel framework for disinfection analisys in drinking water networks

Disinfection in drinking water networks is performed to ensure water safety and potability. However, disinfectants can react with organic compounds present in the water networks. The reaction of disinfectants with such compounds generates byproducts that could be dangerous for human health. The compounds are generally endogenous, i.e., adhered or/and released by the pipe wall, and not introduced from reservoirs because water utilities efficiently monitor and manage their water quality. The present effort, starting from the relevant scientific literature, proposes a novel framework which is based on analysing the disinfectant consumption using a second order kinetic model. The novel framework allows analysing the two endogenous mechanisms of compounds reactions: the first mechanism refers to compounds detached by the momentum of water flow and reacting into the bulk while transported by flow, the second one refers to local reaction at wall. The use of a chemically based second order kinetic model, furthermore, allows for emphasizing the role of stoichiometry and reaction rate constants of reactants and byproducts as relevant parameters to be evaluated at laboratory scale for the specific water system. Thus, the proposed framework can effectively support water quality management using monitoring data together with hydraulic modelling. Two real water distribution systems, located in southern Italy and managed by Acquedotto Pugliese, were used as case studies: the small-sized network of Monteparano and the large-sized one of Bari.

physics.chem-ph↗

arXiv2023

Hierarchical physically based machine learning in material science: the case study of spider silk

Multiscale phenomena exhibit complex structure-function relationships, and predicting their macroscopic behavior requires deducing differential equations at different scales. The complexity of these equations and the number of essential parameters make developing effective, predictive models challenging. To overcome this, researchers explore leveraging advanced numerical techniques from artificial intelligence and machine learning. Here, we focus on a fundamental aspect in multiscale phenomena, i.e the recognition of the hierarchical role of variables. By adopting a Pareto front interpretation, we aim to deduce simple and accurate relations for material modeling, starting from experimental multiscale analyses. From a physical point of view, the aim is to deduce information at higher scales from lower scales data, possibly respecting their hierarchical order. A crucial aspect of the proposed approach is the deduction of causality relations among the different variables to be compared with the available theoretical notions and possibly new interpretations resulting by the data modelling. This result in a stepwise approximation going from data modelling to theoretical equations and back to data modelling. To demonstrate the key advantages of our multiscale numerical approach, compared to classical, non-physically based data modelling techniques, we consider the explicit example of spider silk, known for its exceptional properties and bioinspiration potential. Indeed, it presents a complex behavior resulting from mesostructures formed by the aggregation of amino acids at the molecular scale. We argue that, due to the generality of our results, our approach may represent a proof of concept in many fields where multiscale, hierarchical differential equations regulate the observed phenomenon.

math-ph↗

arXiv2020

Embedding the intrinsic relevance of vertices in network analysis: the case of centrality metrics

Complex network theory (CNT) is gaining a lot of attention in the scientific community, due to its capability to model and interpret an impressive number of natural and anthropic phenomena. One of the most active CNT field concerns the evaluation of the centrality of vertices and edges in the network. Several metrics have been proposed, but all of them share a topological point of view, namely centrality descends from the local or global connectivity structure of the network. However, vertices can exhibit their own intrinsic relevance independent from topology; e.g., vertices representing strategic locations (e.g., hospitals, water and energy sources, etc.) or institutional roles (e.g., presidents, agencies, etc.). In these cases, the connectivity network structure and vertex intrinsic relevance mutually concur to define the centrality of vertices and edges. The purpose of this work is to embed the information about the intrinsic relevance of vertices into CNT tools to enhance the network analysis. We focus on the degree, closeness and betweenness metrics, being among the most used. Two examples, concerning a social (the historical Florence family marriage network) and an infrastructure (a water supply system) network, demonstrate the effectiveness of the proposed relevance-embedding extension of the centrality metrics.

cs.SI↗