Shelf Life Assessment of Fresh Poultry Meat Packaged in Novel Bionanocomposite of Chitosan Incorporated with ZnO nanoparticles synthesized using food industry by-products

Nel settore alimentare viene utilizzata un’elevata quantità di materie plastiche per conservare i prodotti e facilitarne la distribuzione. L’utilizzo di questi polimeri ha un costo ambientale piuttosto elevato, per questo trovare surrogati ecosostenibili diventa sempre più importante. In questa tesi abbiamo testato l’efficacia del confezionamento di un prodotto altamente deperibile, quale carne di pollo, con un biofilm a base di chitosano. Il chitosano è polisaccaride largamente presente in natura, dotato di caratteristiche chimico-fisiche che permettono l’ottenimento di un film con proprietà meccaniche e di barriera simili ai polimeri tradizionali, oltre a possedere attività antibatterica. Abbiamo realizzato film contenenti chitosano e altri biocomposti, quali montmorillonite, nanoparticelle di ossido di zinco e olio essenziale di rosmarino, per un totale di 6 film con diversa composizione. Tramite analisi microbiologiche e chimico-fisiche abbiamo confrontato l’efficacia dei diversi film prodotti rispetto ad un controllo (carne conservata in un contenitore asettico). Le analisi sono state svolte in doppio, a 0, 3, 7, 10, 15 giorni di conservazione ad una temperatura di 4°C. In diversi film abbiamo ottenuto una riduzione significativa rispetto al controllo (p<0,05) della conta totale dei microrganismi mesofili aerobici (TMAM) e delle Enterobacteriaceae. La rilevazione del pH e dell’acidità titolabile ha fornito risultati in linea a quelli microbiologici. I campioni nel biofilm hanno spesso subito una variazione significativa (p<0,05) dell’umidità rispetto al controllo, a causa dell’elevata permeabilità al vapore acqueo. L’analisi dei TBARS non ha spesso riportato differenze significative rispetto al controllo (p>0,05), e quando presenti, è perché il campione era più ossidato del controllo (p<0,05). Invece, è stato ottenuto un miglioramento significativo (p<0,05) dello Hue angle tra i film e il controllo. I risultati ottenuti forniscono le basi per studi aggiuntivi.

Characterization Of Single Use Reactor Used For The Manufacturing Of Formulated Products Within The Pharmaceutical Industry

Mixing is a fundamental unit operation in the pharmaceutical industry to ensure consistent product quality across different batches. It is usually carried out in mechanically stirred tanks, with a large variety of designs according to the process requirements. A key aspect of pharmaceutical manufacturing is the extensive and meticulous cleaning of the vessels between runs to prevent the risk of contamination. Single-use reactors represent an increasing trend in the industry since they do not require cleaning and sterilization, reducing the need for utilities such as steam to sterilize equipment and the time between production batches. In contrast to traditional stainless steel vessels, single-use reactors consist of a plastic bag used as a vessel and disposed of after use. This thesis aims to characterize the fluid dynamics features and the mixing performance of a commercially available single-use reactor. The characterization employs a combination of various experimental techniques. The analysis starts with the visual observation of the liquid behavior inside the vessel, focusing on the vortex shape evolution at different impeller speeds. The power consumption is then measured using a torque meter to quantify the power number. Particle Image Velocimetry (PIV) is employed to investigate local fluid dynamics properties such as mean flow field and mean and rms velocity profiles. The same experimental setup of PIV is exploited for another optical measurement technique, the Planar Laser-Induced Fluorescence (PLIF). The PLIF measurements complete the characterization of the reactor with the qualitative visualization of the turbulent flow and the quantitative assessment of the system performance through the mixing time. The results confirm good mixing performances for the single-use reactor over the investigated impeller speeds and reveal that the filling volume plays a significant role in the fluid dynamics of the system.

Graph-based Vision-Language Model for Multi-Modal Retrieval of Fashion Products

Artificial Intelligence is reshaping the field of fashion industry in different ways. E-commerce retailers exploit their data through AI to enhance their search engines, make outfit suggestions and forecast the success of a specific fashion product. However, it is a challenging endeavour as the data they possess is huge, complex and multi-modal. The most common way to search for fashion products online is by matching keywords with phrases in the product's description which are often cluttered, inadequate and differ across collections and sellers. A customer may also browse an online store's taxonomy, although this is time-consuming and doesn't guarantee relevant items. With the advent of Deep Learning architectures, particularly Vision-Language models, ad-hoc solutions have been proposed to model both the product image and description to solve this problems. However, the suggested solutions do not exploit effectively the semantic or syntactic information of these modalities, and the unique qualities and relations of clothing items. In this work of thesis, a novel approach is proposed to address this issues, which aims to model and process images and text descriptions as graphs in order to exploit the relations inside and between each modality and employs specific techniques to extract syntactic and semantic information. The results obtained show promising performances on different tasks when compared to the present state-of-the-art deep learning architectures.

Numerical modeling on the short-term response of fiber-reinforced concrete round panels

Fiber-reinforced concrete is a composite material consisting of discrete, discontinuous, and uniformly distributed fibers in plain concrete primarily used to enhance the tensile properties of the concrete. FRC performance depends upon the fiber, interface, and matrix properties. The use of fiber-reinforced concrete has been increasing substantially in the past few years in different fields of the construction industry such as ground-level application in sidewalks and building floors, tunnel lining, aircraft parking, runways, slope stabilization, etc. Many experiments have been performed to observe the short-term and long-term mechanical behavior of fiber-reinforced concrete in the last decade and numerous numerical models have been formulated to accurately capture the response of fiber-reinforced concrete. The main purpose of this dissertation is to numerically calibrate the short-term response of the concrete and fiber parameters in mesoscale for the three-point bending test and cube compression test in the MARS framework which is based on the lattice discrete particle model (LDPM) and later validate the same parameters for the round panels. LDPM is the most validated theory in mesoscale theories for concrete. Different seeds representing the different orientations of concrete and fiber particles are simulated to produce the mean numerical response. The result of numerical simulation shows that the lattice discrete particle model for fiber-reinforced concrete can capture results of experimental tests on the behavior of fiber-reinforced concrete to a great extent.