Caratterizzazione delle Prestazioni di Protezione al Fuoco di Vernici Intumescenti

Le pitture intumescenti sono utilizzate come protettivi passivi antincendio nel settore delle costruzioni. In particolare sono utilizzate per aumentare la resistenza al fuoco di elementi in acciaio. Le proprietà termiche di questi rivestimenti sono spesso sconosciute o difficili da stimare per via del fatto che variano notevolmente durante il processo di espansione che subisce l’intumescente quando esposto al calore di un incendio. Per questa ragione la validazione della resistenza al fuoco di un rivestimento presente in commercio si basa su metodi costosi economicamente e come tempi di esecuzione nel quale ciascuna trave e colonna rivestita di protettivo deve essere testata una alla volta attraverso il test di resistenza al fuoco della curva cellulosica. In questo lavoro di tesi adottando invece un approccio basato sulla modellazione termica del rivestimento intumescente si ottiene un aiuto nella semplificazione della procedura di test ed un supporto nella progettazione della resistenza al fuoco delle strutture. Il tratto di unione nei vari passaggi della presente tesi è stata la metodologia di stima del comportamento termico sconosciuto, tale metodologia di stima è la “Inverse Parameter Estimation”. Nella prima fase vi è stata la caratterizzazione chimico fisica della vernice per mezzo di differenti apparecchiature come la DSC, la TGA e l’FT-IR che ci hanno permesso di ottenere la composizione qualitativa e le temperature a cui avvengono i principali processi chimici e fisici che subisce la pittura come anche le entalpie legate a questi eventi. Nella seconda fase si è proceduto alla caratterizzazione termica delle pitture al fine di ottenerne il valore di conduttività termica equivalente. A tale scopo si sono prima utilizzate le temperature dell’acciaio di prove termiche alla fornace con riscaldamento secondo lo standard ISO-834 e successivamente per meglio definire le condizioni al contorno si è presa come fonte di calore un cono calorimetrico in cui la misura della temperatura avveniva direttamente nello spessore del’intumescente. I valori di conduttività ottenuti sono risultati congruenti con la letteratura scientifica e hanno mostrato la dipendenza della stessa dalla temperatura, mentre si è mostrata poco variante rispetto allo spessore di vernice deposto ed alla geometria di campione utilizzato.

Monitoring Infants By Automatic Video Processing

This work has, as its objective, the development of non-invasive and low-cost systems for monitoring and automatic diagnosing specific neonatal diseases by means of the analysis of suitable video signals. We focus on monitoring infants potentially at risk of diseases characterized by the presence or absence of rhythmic movements of one or more body parts. Seizures and respiratory diseases are specifically considered, but the approach is general. Seizures are defined as sudden neurological and behavioural alterations. They are age-dependent phenomena and the most common sign of central nervous system dysfunction. Neonatal seizures have onset within the 28th day of life in newborns at term and within the 44th week of conceptional age in preterm infants. Their main causes are hypoxic-ischaemic encephalopathy, intracranial haemorrhage, and sepsis. Studies indicate an incidence rate of neonatal seizures of 0.2% live births, 1.1% for preterm neonates, and 1.3% for infants weighing less than 2500 g at birth. Neonatal seizures can be classified into four main categories: clonic, tonic, myoclonic, and subtle. Seizures in newborns have to be promptly and accurately recognized in order to establish timely treatments that could avoid an increase of the underlying brain damage. Respiratory diseases related to the occurrence of apnoea episodes may be caused by cerebrovascular events. Among the wide range of causes of apnoea, besides seizures, a relevant one is Congenital Central Hypoventilation Syndrome (CCHS) \cite{Healy}. With a reported prevalence of 1 in 200,000 live births, CCHS, formerly known as Ondine's curse, is a rare life-threatening disorder characterized by a failure of the automatic control of breathing, caused by mutations in a gene classified as PHOX2B. CCHS manifests itself, in the neonatal period, with episodes of cyanosis or apnoea, especially during quiet sleep. The reported mortality rates range from 8% to 38% of newborn with genetically confirmed CCHS. Nowadays, CCHS is considered a disorder of autonomic regulation, with related risk of sudden infant death syndrome (SIDS). Currently, the standard method of diagnosis, for both diseases, is based on polysomnography, a set of sensors such as ElectroEncephaloGram (EEG) sensors, ElectroMyoGraphy (EMG) sensors, ElectroCardioGraphy (ECG) sensors, elastic belt sensors, pulse-oximeter and nasal flow-meters. This monitoring system is very expensive, time-consuming, moderately invasive and requires particularly skilled medical personnel, not always available in a Neonatal Intensive Care Unit (NICU). Therefore, automatic, real-time and non-invasive monitoring equipments able to reliably recognize these diseases would be of significant value in the NICU. A very appealing monitoring tool to automatically detect neonatal seizures or breathing disorders may be based on acquiring, through a network of sensors, e.g., a set of video cameras, the movements of the newborn's body (e.g., limbs, chest) and properly processing the relevant signals. An automatic multi-sensor system could be used to permanently monitor every patient in the NICU or specific patients at home. Furthermore, a wire-free technique may be more user-friendly and highly desirable when used with infants, in particular with newborns. This work has focused on a reliable method to estimate the periodicity in pathological movements based on the use of the Maximum Likelihood (ML) criterion. In particular, average differential luminance signals from multiple Red, Green and Blue (RGB) cameras or depth-sensor devices are extracted and the presence or absence of a significant periodicity is analysed in order to detect possible pathological conditions. The efficacy of this monitoring system has been measured on the basis of video recordings provided by the Department of Neurosciences of the University of Parma. Concerning clonic seizures, a kinematic analysis was performed to establish a relationship between neonatal seizures and human inborn pattern of quadrupedal locomotion. Moreover, we have decided to realize simulators able to replicate the symptomatic movements characteristic of the diseases under consideration. The reasons is, essentially, the opportunity to have, at any time, a 'subject' on which to test the continuously evolving detection algorithms. Finally, we have developed a smartphone App, called 'Smartphone based contactless epilepsy detector' (SmartCED), able to detect neonatal clonic seizures and warn the user about the occurrence in real-time.