Integrando le Smart Water Network con algoritmi di intelligenza computazionale per una stima affidabile della qualità delle acque nei sistemi acquedottistici

Attuali studi scientifici nel campo dei sistemi di adduzioni e distribuzione delle acque potabili hanno mostrato un notevole interesse verso soluzioni e tecnologie “smart”, mirate al miglioramento del controllo e della gestione della reti idriche, note come Smart Water Network (SWN). Tali soluzioni nascono allo scopo di ottenere un monitoraggio in continuo e distribuito dell’intera rete acquedottistica, fornire in tempo reale informazioni sul comportamento idraulico e qualitativo delle reti e quindi mirare all’ottimizzazione della gestione del controllo dei servizi idrici. Le SWN sono generalmente ottenute utilizzando diversi strumenti e tecnologie quali reti di monitoraggio, sistemi di acquisizione e controllo (SCADA), sistemi informativi territoriali (GIS/SWE) e modellistica numerica di simulazione. In questo contesto, il presente lavoro di ricerca, partendo dalle tecnologie e dagli strumenti tipici delle SWN, ha come obiettivo quello di migliorare la stima della qualità delle acque, formulando problemi di ottimizzazione mono- e multi-obiettivo e risolvendo quest’ultimi tramite l’uso di algoritmi evolutivi. La metodologia innovativa sviluppata è stata applicata ad un acquedotto reale quale il Santa Sofia, acquedotto gestito da Acqua Campania Spa, che convoglia e distribuisce direttamente la risorsa idrica nella rete acquedottistica cittadina di diversi comuni casertani connessi alla rete. Tale acquedotto è monitorato in continuo da una rete di sonde multi-parametriche per la misura dei parametri quali/quantitativi lungo l’intero acquedotto. Al fine di ottenere una stima affidabile della qualità delle acque sono stati messi a punto processi di auto-calibrazione periodica sia per il modello idraulico che per i modelli di qualità delle acque, formulando problemi di ottimizzazione che massimizzano l’accuratezza (minimizzano l’incertezza) della modellistica di simulazione. La risoluzione di tali problemi di ottimizzazione è stata ottenuta utilizzando algoritmi genetici. I dati misurati in continuo dalla rete di sensori distribuita sono automaticamente utilizzati nel processo di calibrazione, realizzando un’integrazione tra la rete di monitoraggio e la modellistica di simulazione. Sono stati definiti schemi ottimali di posizionamento di sensori multi-parametrici lungo un acquedotto al fine di ottimizzare i processi di calibrazione della modellistica idraulica e di qualità attraverso la formulazione di problemi di ottimizzazione multi-obiettivo, risolti con algoritmi MOGA (Multi Objective Genetic Algorithm). La metodologia messa a punto è stata sperimentata sia per la validazione della rete di monitoraggio esistente sull’acquedotto pilota che per definizione di nuovi schemi di campionamento. Le tecniche di localizzazione ottimale dei sensori e le procedure di auto-calibrazione periodica hanno permesso di ottenere una stima affidabile del decadimento del cloro e della formazione dei DBPs, distribuita e in continuo lungo l'intero sistema acquedottistico pilota Santa Sofia. Attraverso una console GIS, sono state eseguite le simulazioni e visualizzati i risultatati attraverso mappe tematiche, consentendo di individuare in modo immediato e tempestivo segmenti dell’acquedotto caratterizzati da concentrazioni fuori norma. In tale console è stata realizzata l’integrazione tra la rete di monitoraggio del Santa Sofia e la modellistica, permettendo di assimilare (data assimilation) i dati di cloro acquisiti dai sensori nell’elaborazione degli scenari di previsione. La metodologia sviluppata permette di ottenere una stima affidabile della qualità delle acque, ovvero del decadimento del cloro e della formazione dei DBPs, in continuo e distribuita lungo un intero sistema acquedottistico. La visualizzazione in ambiente GIS garantisce un'immediata valutazione di eventuali anomalie supportando una gestione ottimizzata della rete. La metodologia proposta può ritenersi di supporto alle tradizionali tecniche di controllo delle acque potabili per una gestione ottimizzata delle reti idriche

Old-fashioned and newly discovered biomarkers: the future of NAFLD-related HCC screening and monitoring

Nonalcoholic fatty liver disease (NAFLD) is the major contributor to the global burden of chronic liver diseases and ranges from simple and reversible steatosis to nonalcoholic steatohepatitis (NASH), which may progress into cirrhosis and hepatocellular carcinoma (HCC). HCC represents the most common liver cancer, and it is a leading cause of death worldwide with an increasing trend for the future. Due to late diagnosis, non-responsiveness to systemic therapy, and high cancer heterogeneity, the treatment of this malignancy is challenging. To date, liver biopsy and ultrasound (US) are the gold standard procedures for HCC diagnosis and surveillance, although they are not suitable for mass screening. Therefore, it is impelling to find new, less invasive diagnostic strategies able to detect HCC at an early stage as well as monitor tumor progression and recurrence. Common and rare inherited variations that boost the switching from NASH to liver cancer may help to predict tumor onset. Furthermore, epigenetic changes which reflect intertumoral heterogeneity occur early in tumorigenesis and are highly stable under pathologic conditions. The severity of hepatic injuries can be detected through the analysis of cell circulating tumor DNAs (ctDNAs), microRNAs (miRNAs), and noncoding RNAs (ncRNAs), which are involved in several pathological processes that feature cancer, including cell growth, survival, and differentiation, thus representing appealing biomarkers for HCC. Therefore, this review discusses the current options for HCC surveillance, focusing on the role of genetic and epigenetic biomarkers as new strategies to refine HCC management

Applying Numerical Models and Optimized Sensor Networks for Drinking Water Quality Control

AbstractDrinking water distribution networks must provide safe water to the consumers in adequate quantity and quality. In this framework, the present research work investigates an integrated approach for drinking water quality control by applying hydraulic and water quality models to a real aqueduct. The results of the model simulations allow identifying the optimal locations of monitoring stations in order to achieve an effective contaminant detection, and to ensure the maximum protection of the consumers health. The methodology is applied to a case study, referring to a real aqueduct located in Campania (South Italy)

Cooperative Air Quality Sensing with Crowdfunded Mobile Chemical Multisensor Devices

In this work, we describe a cooperative air quality sensor architecture based on crowdfunded, mobile, electrochemical sensor based, monitoring systems. The platform aims to produce enhanced information on personal pollutant exposure and enable cooperative reconstruction of high resolution pictures of air pollution in the urban landscape. The calibrated devices are connected to smartphones that provide georeferenced visualization of personal exposure and session based log capabilities. A cloud based interface provides a sensor fusion based mapping capability exploiting google maps APIs. An in-lab calibration by linear regression with temperature correction has been computed and preliminary results have been reported. A small set of calibrated devices will be shipped to crowdfunders for extended field tests in different italian cities

A Sensor Fusion Method Applied to Networked Rain Gauges for Defining Statistically Based Rainfall Thresholds for Landslide Triggering

Timely alerts provided to the communities at risk of landslides can prevent casualties and costly damages to people, buildings and infrastructures. The rainfalls are one of the primary triggering causes for landslides so that empirical approaches based on the correlation between landslides occurrence and rainfall characteristics, are considered effective for warning systems. This research work has intended to develop a landslide alerting system by using a Sensor Fusion method based on the SVC (Support Vector Classification) techniques. This method fuses rainfall data gathered in continuous by networked rain gauges and returns confidence degrees associated to the not occurrence of the landslide event as well as to the occurrence of one. By using a k-fold validation technique, an SVC-model, with AUC (Area Under the Curve) mean of 0,964733 and variance of 0,001243, has been defined. The proposed method has been tested on the regional rain gauges network, deployed in Calabria (Italy)

Optimal Sensors Placement for Flood Forecasting Modelling

Numerical models are instrumental to more effective flood forecasting and managementservices though they suffer from numerous uncertaintysources. An effective model calibration is hence essential. In this research work,a methodology of optimal sampling design has been investigated and developed forwater drainage networks. Optimal hydrometer sensors locationsalong the Amato River (South Italy)have been defined by optimizing a two-objective function that maximizes the calibrated model accuracy and minimizesthe total metering cost. This problem has been solved by using an enumerative search solution, run on the ENEA/CRESCO HPC infrastructure, evaluating the exact Pareto-front byefficient computational time

Surgeons’ practice and preferences for the anal fissure treatment: results from an international survey

The best nonoperative or operative anal fissure (AF) treatment is not yet established, and several options have been proposed. Aim is to report the surgeons' practice for the AF treatment. Thirty-four multiple-choice questions were developed. Seven questions were about to participants' demographics and, 27 questions about their clinical practice. Based on the specialty (general surgeon and colorectal surgeon), obtained data were divided and compared between two groups. Five-hundred surgeons were included (321 general and 179 colorectal surgeons). For both groups, duration of symptoms for at least 6 weeks is the most important factor for AF diagnosis (30.6%). Type of AF (acute vs chronic) is the most important factor which guide the therapeutic plan (44.4%). The first treatment of choice for acute AF is ointment application for both groups (59.6%). For the treatment of chronic AF, this data is confirmed by colorectal surgeons (57%), but not by the general surgeons who prefer the lateral internal sphincterotomy (LIS) (31.8%) (p = 0.0001). Botulin toxin injection is most performed by colorectal surgeons (58.7%) in comparison to general surgeons (20.9%) (p = 0.0001). Anal flap is mostly performed by colorectal surgeons (37.4%) in comparison to general surgeons (28.3%) (p = 0.0001). Fissurectomy alone is statistically significantly most performed by general surgeons in comparison to colorectal surgeons (57.9% and 43.6%, respectively) (p = 0.0020). This analysis provides useful information about the clinical practice for the management of a debated topic such as AF treatment. Shared guidelines and consensus especially focused on operative management are required to standardize the treatment and to improve postoperative results