Monitoring the extracellular matrix mineralization processes in biological scaffolds using bioreactors, x-ray μct technology and 3-d modeling methods (monitoraggio dei processi di mineralizzazione della matrice extracellulare in scaffolds biologici, utilizzando bioreattore, tecnologia μct a raggi x e metodi di modellizzazione 3d)

Al fine di migliorare le tecniche di coltura cellulare in vitro, sistemi a bioreattore sono sempre maggiormente utilizzati, e.g. ingegnerizzazione del tessuto osseo. Spinner Flasks, bioreattori rotanti e sistemi a perfusione di flusso sono oggi utilizzati e ogni sistema ha vantaggi e svantaggi. Questo lavoro descrive lo sviluppo di un semplice bioreattore a perfusione ed i risultati della metodologia di valutazione impiegata, basata su analisi μCT a raggi-X e tecniche di modellizzazione 3D. Un semplice bioreattore con generatore di flusso ad elica è stato progettato e costruito con l'obiettivo di migliorare la differenziazione di cellule staminali mesenchimali, provenienti da embrioni umani (HES-MP); le cellule sono state seminate su scaffold porosi di titanio che garantiscono una migliore adesione della matrice mineralizzata. Attraverso un microcontrollore e un'interfaccia grafica, il bioreattore genera tre tipi di flusso: in avanti (senso orario), indietro (senso antiorario) e una modalità a impulsi (avanti e indietro). Un semplice modello è stato realizzato per stimare la pressione generata dal flusso negli scaffolds (3•10-2 Pa). Sono stati comparati tre scaffolds in coltura statica e tre all’interno del bioreattore. Questi sono stati incubati per 21 giorni, fissati in paraformaldehyde (4% w/v) e sono stati soggetti ad acquisizione attraverso μCT a raggi-X. Le immagini ottenute sono state poi elaborate mediante un software di imaging 3D; è stato effettuato un sezionamento “virtuale” degli scaffolds, al fine di ottenere la distribuzione del gradiente dei valori di grigio di campioni estratti dalla superficie e dall’interno di essi. Tale distribuzione serve per distinguere le varie componenti presenti nelle immagini; in questo caso gli scaffolds dall’ipotetica matrice cellulare. I risultati mostrano che sia sulla superficie che internamente agli scaffolds, mantenuti nel bioreattore, è presente una maggiore densità dei gradienti dei valori di grigio ciò suggerisce un migliore deposito della matrice mineralizzata. Gli insegnamenti provenienti dalla realizzazione di questo bioreattore saranno utilizzati per progettare una nuova versione che renderà possibile l’analisi di più di 20 scaffolds contemporaneamente, permettendo un’ulteriore analisi della qualità della differenziazione usando metodologie molecolari ed istochimiche.

A deep learning model for controlling the galvanizing process in a production line

In the industry of steelmaking, the process of galvanizing is a treatment which is applied to protect the steel from corrosion. The air knife effect (AKE) occurs when nozzles emit a steam of air on the surfaces of a steel strip to remove excess zinc from it. In our work we formalized the problem to control the AKE and we implemented, with the R&D dept.of MarcegagliaSPA, a DL model able to drive the AKE. We call it controller. It takes as input the tuple : a tuple of the physical conditions of the process line (t,h,s) with the target value of the zinc coating (c); and generates the expected tuple of (pres and dist) to drive the mechanical nozzles towards the (c). According to the requirements we designed the structure of the network. We collected and explored the data set of the historical data of the smart factory. Finally, we designed the loss function as sum of three components: the minimization between the coating addressed by the network and the target value we want to reach; and two weighted minimization components for both pressure and distance. In our solution we construct a second module, named coating net, to predict the coating of zinc resulting from the AKE when the conditions are applied to the prod. line. Its structure is made by a linear and a deep nonlinear “residual” component learned by empirical observations. The predictions made by the coating nets are used as ground truth in the loss function of the controller. By tuning the weights of the different components of the loss function, it is possible to train models with slightly different optimization purposes. In the tests we compared the regularization of different strategies with the standard one in condition of optimal estimation for both; the overall accuracy is ± 3 g/m^2 dal target for all of them. Lastly, we analyze how the controller modeled the current solutions with the new logic: the sub-optimal values of pres and dist can be optimize of 50% and 20%.

A new method for continuous quality control of nsm cfrp systems

In the last decade the near-surface mounted (NSM) strengthening technique using carbon fibre reinforced polymers (CFRP) has been increasingly used to improve the load carrying capacity of concrete members. Compared to externally bonded reinforcement (EBR), the NSM system presents considerable advantages. This technique consists in the insertion of carbon fibre reinforced polymer laminate strips into pre-cut slits opened in the concrete cover of the elements to be strengthened. CFRP reinforcement is bonded to concrete with an appropriate groove filler, typically epoxy adhesive or cement grout. Up to now, research efforts have been mainly focused on several structural aspects, such as: bond behaviour, flexural and/or shear strengthening effectiveness, and energy dissipation capacity of beam-column joints. In such research works, as well as in field applications, the most widespread adhesives that are used to bond reinforcements to concrete are epoxy resins. It is largely accepted that the performance of the whole application of NSM systems strongly depends on the mechanical properties of the epoxy resins, for which proper curing conditions must be assured. Therefore, the existence of non-destructive methods that allow monitoring the curing process of epoxy resins in the NSM CFRP system is desirable, in view of obtaining continuous information that can provide indication in regard to the effectiveness of curing and the expectable bond behaviour of CFRP/adhesive/concrete systems. The experimental research was developed at the Laboratory of the Structural Division of the Civil Engineering Department of the University of Minho in Guimar\~aes, Portugal (LEST). The main objective was to develop and propose a new method for continuous quality control of the curing of epoxy resins applied in NSM CFRP strengthening systems. This objective is pursued through the adaptation of an existing technique, termed EMM-ARM (Elasticity Modulus Monitoring through Ambient Response Method) that has been developed for monitoring the early stiffness evolution of cement-based materials. The experimental program was composed of two parts: (i) direct pull-out tests on concrete specimens strengthened with NSM CFRP laminate strips were conducted to assess the evolution of bond behaviour between CFRP and concrete since early ages; and, (ii) EMM-ARM tests were carried out for monitoring the progressive stiffness development of the structural adhesive used in CFRP applications. In order to verify the capability of the proposed method for evaluating the elastic modulus of the epoxy, static E-Modulus was determined through tension tests. The results of the two series of tests were then combined and compared to evaluate the possibility of implementation of a new method for the continuous monitoring and quality control of NSM CFRP applications.

The use of remote sensing imagery for monitoring recovery in the aftermath of a natural disaster: the Hurricane Katrina Case

Although Recovery is often defined as the less studied and documented phase of the Emergency Management Cycle, a wide literature is available for describing characteristics and sub-phases of this process. Previous works do not allow to gain an overall perspective because of a lack of systematic consistent monitoring of recovery utilizing advanced technologies such as remote sensing and GIS technologies. Taking into consideration the key role of Remote Sensing in Response and Damage Assessment, this thesis is aimed to verify the appropriateness of such advanced monitoring techniques to detect recovery advancements over time, with close attention to the main characteristics of the study event: Hurricane Katrina storm surge. Based on multi-source, multi-sensor and multi-temporal data, the post-Katrina recovery was analysed using both a qualitative and a quantitative approach. The first phase was dedicated to the investigation of the relation between urban types, damage and recovery state, referring to geographical and technological parameters. Damage and recovery scales were proposed to review critical observations on remarkable surge- induced effects on various typologies of structures, analyzed at a per-building level. This wide-ranging investigation allowed a new understanding of the distinctive features of the recovery process. A quantitative analysis was employed to develop methodological procedures suited to recognize and monitor distribution, timing and characteristics of recovery activities in the study area. Promising results, gained by applying supervised classification algorithms to detect localization and distribution of blue tarp, have proved that this methodology may help the analyst in the detection and monitoring of recovery activities in areas that have been affected by medium damage. The study found that Mahalanobis Distance was the classifier which provided the most accurate results, in localising blue roofs with 93.7% of blue roof classified correctly and a producer accuracy of 70%. It was seen to be the classifier least sensitive to spectral signature alteration. The application of the dissimilarity textural classification to satellite imagery has demonstrated the suitability of this technique for the detection of debris distribution and for the monitoring of demolition and reconstruction activities in the study area. Linking these geographically extensive techniques with expert per-building interpretation of advanced-technology ground surveys provides a multi-faceted view of the physical recovery process. Remote sensing and GIS technologies combined to advanced ground survey approach provides extremely valuable capability in Recovery activities monitoring and may constitute a technical basis to lead aid organization and local government in the Recovery management.