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A high-performance code for the use of spectral POD on the analysis of turbomachinery high fidelity simulations
No full textRegenerative practices reduce global warming impact and intensity of maize systems in north-central Italy
No full textSize-dependent magnetic properties of NiO nanoparticles synthesized via Ni–hydroxyacetate decomposition
No full textThis study focuses on a systematic investigation of the structural and magnetic properties of NiO nanoparticles (NPs) synthesized via the thermal dehydroxylation of a polyol-derived nickel layered hydroxyacetate salt (Ni–LHS). The turbostratic brucite-like Ni–LHS precursor undergoes a phase transition into pure NiO at 260–600 °C, yielding single-phase NPs with crystallite sizes ranging from ∼4.5 to 18 nm. Structural analyses reveal a transition from a sheet-like to a rougly spherical morphology and a progressive lattice contraction with increasing annealing temperature. Magnetic measurements at 5 K reveal characteristic signatures of nanoscale antiferromagnetism, including unsaturated hysteresis loops, high coercivity values, and pronounced exchange bias (EB). Both coercivity and EB exhibit a non-monotonic size dependence, reflecting a crossover from surface-disordered to core-ordered magnetic behaviour. These findings provide new insights into the interplay between finite-size effects and interfacial anisotropy in antiferromagnetic NiO NPs
Development and experimentation of an underwater reconfigurable vehicle for survey, inspection and intervention
No full textThe increasing demand for flexible and cost-effective solutions in underwater exploration and maintenance has driven the development of innovative marine robotic systems capable of adapting to a wide range of operational scenarios. This thesis presents the design, development, and experimental validation of the RUVIFIST (Reconfigurable Underwater Vehicle for Inspection, Free-floating Intervention, and Survey Tasks) vehicle, a novel Autonomous Underwater Reconfigurable Vehicle (AURV) capable of autonomously changing its shape to optimize performance across different mission objective.
The RUVIFIST vehicle is developed to fill the gap between the traditional Remotely Operated Vehicles (ROVs), which offer high maneuverability but limited autonomy, and Autonomous Underwater Vehicles (AUVs), which excel in long-range endurance but lack precise control in in-site operations. The vehicle achieves this duality by exploiting a reconfigurable structure that allows it to operate both in a streamlined ``survey'' configuration and a compact "hovering" configuration, suitable for inspection and intervention tasks.
The research encompasses multiple aspects of underwater robotics. The first part of the work presents the mechanical and software design of the RUVIFIST vehicle, focusing on modular interconnections, watertight enclosures, and a ROS-based architecture for distributed control and communication. The second part introduces the Guidance, Navigation, and Control (GNC) framework, including a Gain-Scheduled PID (GS-PID) controller to maintain performance consistency across different configurations. A Physics-Informed Neural Network (PINN) was developed to estimate hydrodynamic parameters such as added mass, and linear drag coefficients directly from on-field data, improving the fidelity of the dynamic model and the feed-forward control implemented along side the GS-PID. In parallel, an Autoencoder-based Fault Detection and Identification (FDI) system was implemented to detect and classify actuator or sensor faults in real time, increasing mission safety and robustness.
Experimental validation was carried out both in controlled and open-water environments, confirming the vehicle’s capability to autonomously reconfigure its geometry and maintain stable navigation during transitions. Results aim to demonstrate accurate dynamic parameter estimation, enhanced control performance and also highlight the difference, in terms of added mass and linear drag, between "survey" and
"hovering" configuration of the vehicle.
In conclusion, the thesis contributes to the field of marine robotics by delivering one of the first fully operational prototypes of a reconfigurable underwater vehicle capable of performing both survey and intervention tasks thanks to the capability of autonomously change its shape. The combination of modular design, adaptive control, and AI-driven perception establishes a foundation for next-generation AURVs that can perform long-term resident operations, thus reducing deployment costs and extending the capabilities of autonomous subsea systems
Design of new molecules as a therapeutic strategy for Cystic Fibrosis
No full textThe approval of Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) modulator therapy by global health agencies over the past few decades has fundamentally changed the course of patients affected by Cystic Fibrosis (CF). CFTR is a membrane protein that functions as a chloride channel in epithelial cells. Mutations in the CFTR gene lead to absent or impaired chloride transport across the apical membranes, primarily in the respiratory and glandular epithelia. This defect results in the extracellular accumulation of thick, sticky mucus, leading to chronic conditions such as sinusitis, bronchitis, pneumonia, and asthma, often refractory to standard therapy, as well as nasal polyposis, digital clubbing, and bronchiectasis.
CFTR modulators are categorized, based on their mechanism of action, as correctors, which enhance CFTR folding and trafficking to the cell surface, and potentiators, which improve ion transport through CFTR channels already present on the apical membrane. The four currently approved modulators are the potentiator Ivacaftor (VX770 or IVA) and the correctors Lumacaftor (VX809 or LUM), Tezacaftor (VX661 or TEX), and Elexacaftor (VX445 or ELX). Combinations of these modulators, such as the triple combination Ivacaftor/Tezacaftor/Elexacaftor (Kaftrio®), represent the best therapeutic option available for patients with sensitive CFTR mutations, including the most common one, the phenylalanine 508 deletion (F508del-CFTR). However, current therapies do not guarantee complete CFTR functional recovery, highlighting the need for developing new molecules to fill these therapeutic gaps.
This thesis reports on the development and study of novel molecules featuring an arylthiazole scaffold for their potential to restore CFTR functionality. The goal was to develop compounds that are more active than those already clinically approved, both when administered alone and in combination, aiming for a synergistic effect due to complementary mechanisms of action between them and already approved modulators.
Functional channel activity assays using the Yellow Fluorescent Protein (YFP) reporter in Fisher Rat Thyroid (FRT) and CFBE41o- cells stably expressing F508del-CFTR demonstrated a significant additive/synergistic effect when the novel VX809-hybrids 2a and 3a were combined with VX445.
Furthermore, eight other newly synthesized compounds (4a, 5a, 1b, 2b, 3b, 7b, and 1c), when tested individually, significantly increased F508del-CFTR activity compared to the vehicle treatment in CFBE bronchial epithelial cells. Notably, the combinations 2b + 5a and 7b + 4a resulted in
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approximately 20% and 29% greater F508del-CFTR rescue, respectively, than the clinically used VX809 + VX445 combination.
To confirm this efficacy, a functional measurement of CFTR activity using the Fluorescence Microplate Polarimetry (FMP) assay was performed in Human Nasal Epithelial (HNE) primary cells from four CF patients homozygous for the F508del-CFTR mutation. The results confirmed that the novel corrector combinations (2b + 5a and 7b + 4a) significantly increased FSK-activated and VX770-potentiated F508del-CFTR function compared to the rescue levels mediated by either the VX809 + VX445 or VX661 + VX445 combinations in HNE cells.
Finally, recognizing the emerging role of molecular chaperone modulators as a strategy to enhance F508del-CFTR rescue, combining them with already approved modulators, a small library of Hsc70/Hsp70 modulators was developed based on the scaffold of MKT-077, a known allosteric Hsp70 inhibitor. Among the new library, three MKT-077 analogues, i.e. DL79, DL90, and AP161, showed an inhibitory effect on human recombinant Hsp70 ATPase activity. Significantly, DL79 demonstrated a higher ability than MKT-077 to enhance the corrective effect of VX809 on F508del-CFTR in CFBE41o- cells at a remarkably low concentration. The combination of DL79 with other clinically approved correctors revealed a significant synergistic interaction with VX661 and with the VX661/VX445 combination, though no meaningful effect was observed in combination with VX445 alone.
Results reported in this thesis identified and characterized novel CFTR modulators and molecular chaperone enhancers that demonstrate superior efficacy, particularly in combination, compared to current standard-of-care therapies for F508del-CFTR.
In summary, these results provide strong preclinical evidence supporting the further development of these novel arylthiazole correctors and the Hsp70 modulators as potential next-generation therapeutic agents for CF patients with the F508del-CFTR mutation
On the Approximation of the Shapley Value via Machine Learning in Transportation Network Cooperative Games
No full textThe Shapley value, a well-established concept in cooperative game theory, serves as a metric for assessing the significance of each player in a transferable utility game. Recently, it has found application in gauging the importance of individual nodes or arcs within a network. However, in this context, the exact evaluation of the Shapley value is often computationally expensive, particularly in the case of extensive networks. This study delves into the challenge of approximating the Shapley value in a transferable utility game defined on a network, wherein the characteristics of the network are parameterized by a variable of interest (e.g., the traffic demand). We examine the smoothness of the Shapley value with respect to this parameter and leverage such smoothness to theoretically justify the adoption of machine-learning techniques for its approximate computation. Additionally, we present potential extensions for further research in this area
Public Transport, Accessibility, and User Satisfaction: Improving Equity and Social Well-being
No full textThis chapter focuses on various aspects of local public transport (LPT) with bus services in Italian regions, focusing on the interaction between public transport utilization, user satisfaction, and access to essential services. It highlights the critical role of efficient public transport systems in promoting sustainable development and social well-being in the context of increasing urbanization. Using data from the 2023 ISTAT multi-purpose survey, the study combines variables through the unweighted z-score method to construct three indices: satisfaction, utilization, and ease of access. Spearman’s rank correlation is applied to analyze the relationships among these indices. The findings reveal significant regional disparities in public transport satisfaction and accessibility. Regions such as Trentino Alto Adige/Südtirol and Friuli-Venezia Giulia demonstrate high performance across all indices, serving as examples of best practices. In contrast, regions like Molise and Sicilia face notable challenges in both utilization and satisfaction. The results underscore the importance of improving accessibility to enhance user satisfaction and promote greater public transport usage. The study concludes by emphasizing the need for targeted investments in public transport infrastructure and services to address regional inequalities. By improving accessibility and service quality, policymakers can foster social equity and support sustainable urban development
