Graph neural network methods for representation and generation in drug discovery

Drug discovery is a time-consuming and expensive process, often spanning over a decade and costing billions of dollars. This thesis advances graph-based machine learning approaches to accelerate this process, making three main contributions. First, we provide a comprehensive review of graph neural networks for conditional molecular generation, establishing a framework for understanding and comparing different methods. Building on these insights, we introduce AMCG (Atomic-Molecular Conditional Generator), a novel generative framework that achieves state-of-the-art performance while offering one-shot generation capability and effective property optimization via gradient ascent. Motivated by the heterophilic nature of molecular graphs — where connected atoms often have dissimilar features — we then develop MaxCutPool, a differentiable graph pooling technique based on the MAXCUT problem. By combining graph-theoretical principles with deep learning, MaxCutPool demonstrates superior performance on heterophilic graphs while remaining competitive on standard benchmarks and maintaining computational efficiency. Together, these contributions advance both the theoretical foundations of graph representation learning and provide practical tools for accelerating drug discovery

Innovations in neurological and musculoskeletal rehabilitation: new frontiers in pain management

Background This thesis investigates innovative strategies for managing musculoskeletal disorders, chronic pain, and neurological rehabilitation, combining traditional and digital rehabilitation interventions. Through collaborations with international institutions, the research explores tele-rehabilitation and proprioceptive training aimed at improving functional outcomes and reducing chronic pain. Methods Clinical trials and cross-sectional studies were conducted, including a randomized controlled trial comparing tele-rehabilitation with conventional therapy for knee osteoarthritis. Outcomes were measured using pain, function, and quality of life scales (NPRS, WOMAC, SF-36). In addition, proprioception and balance were assessed in a cross-sectional study on healthy adults. Multimodal pain management strategies were also evaluated, including cognitive-behavioral therapy, neuromodulation (TENS, tDCS), and pharmacological treatments. Results Tele-rehabilitation demonstrated comparable effectiveness to conventional therapy in reducing pain and improving function, with NPRS and WOMAC scores showing significant improvements in both groups (p < 0.001). The proprioception study revealed age-related declines in balance, underscoring the importance of targeted training. Multimodal pain management approaches significantly reduced chronic pain and improved patient adherence to rehabilitation protocols, with neuromodulation techniques (TENS, tDCS) proving particularly effective. Conclusion Tele-rehabilitation emerges as a viable and accessible alternative for managing musculoskeletal disorders. Proprioceptive training is essential, especially for older adults or those with neurological impairments. The findings support the integration of traditional, digital, and psychological approaches to create a more comprehensive and personalized rehabilitation and pain management model

Novel catalytic transformations mediated by dual photoredox and earth abundant transition metals

The main purpose of my doctoral studies was the development of new organic methodologies combining photoredox-and metal catalysis, termed metallaphotoredox catalysis. In this dissertation thesis four novel protocols are reported. Each of them introduces innovative and unprecedented aspects in the metallaphotoredox field. In particular, 1) A dual photoredox-and cobalt-catalyzed protocol for the allylation of carbonyl groups is reported. The innovative aspect is the introduction of a novel TADF (Thermally Activated Delayed Fluorescence)-emitter photocatalyst derived from the degradation of the commercially available 3DPAFIPN, which can sustain the dual catalytic protocol, where the latter and other common TADF-emitters failed. Then, 2) A dual photoredox-and vanadium-catalyzed protocol for the diastereoselective pinacol coupling of aromatic aldehydes is described. The use of vanadium under photoredox conditions for the functionalization of small molecules is an underexplored field, since only few papers are reported in the literature. The key aspects of this work are the high reactivity and the high functional group tolerance of the vanadium complex used. Then, 3) A dual photoredox-and nickel-catalyzed protocol for the diastereoselective allylation of carbonyl groups using Morita-Baylis-Hillman acetates (MBH acetates) is described. The key aspect of this work is the use of MBH acetates as unprecedented pronucleophiles. The protocol was found to be highly regio- and diastereoselective, and tolerant to different functional groups. The products are useful intermediates for the synthesis of bioactive molecules, and few synthetic manipulations of the standard allylated product are reported. Finally, 4) A dual photoredox-and titanium-catalyzed protocol for the allylation of carbonyl groups using 4-bromo crotonates is reported. The key aspect of this work is the use of 4-bromo crotonates as an unprecedented allyl source under photoredox/vinylogous conditions for the fast, highly regioselective and moderately diastereoselective synthesis of α-vinyl-β-hydroxy esters. The presence of multiple functional groups was exploited with some post-functionalization

The physics of blazar jets in the context of multi-wavelength and multi-messenger astronomy

Blazars, active galactic nuclei with relativistic jets pointed to the observer, emit radiation across the spectrum and can produce neutrinos via hadronic processes. Being neutral and nearly massless, neutrinos offer unique insights into energetic astrophysical processes. The flaring gamma-ray blazar TXS0506+056 detected in spatial coincidence with the IceCube-170922A neutrino event confirmed theoretical expectations, emphasising the need for further observational insights into the neutrino-blazar connection. A part of this thesis investigates high-resolution VLBI (parsec-scale) regions of gamma-ray blazars coinciding with IceCube Neutrino Observatory detections. Our VLBI follow-ups aim to identify neutrino-emitting blazars by examining radio properties like coincident flares and jet morphology features. While some of the examined sources show hints of elevated activity at the neutrino arrival, further VLBI and MWL observations are essential for understanding the neutrino production mechanisms in blazars. The low-energy part of blazar broadband SEDs is explained with synchrotron radiation. Regarding the high-energy part, the ongoing debate involves hypotheses on hadronic processes and Inverse Compton scatterings - sometimes with the same low-energy synchrotron photons (SSC models). VHE (E > 100 GeV) long-term monitoring, as with the MAGIC telescopes, complemented with MWL observations, contributes to the comprehensive investigations of blazar emission mechanisms. A part of this thesis analyses a three-year (2020-2022) MWL monitoring of the blazar 1ES1959+650 during a low state. The flux and spectral variability are studied and the SEDs during different low and high VHE states in these three years are interpreted with SSC models. Findings are compared with past active and non-active states. The two parts of this project aim to deepen our understanding of particle acceleration complementarily, using radio and gamma-ray (+MWL) observations, considering future crucial advancements with the SKA and the CTA observatories

Embodied lecturing in engineering in English-Medium Instruction (EMI): exploring the interaction between gestures, (Dis)Fluencies, and pragmatic challenges

This study examines the communicative challenges faced by an Italian first language (L1) lecturer delivering engineering courses through English as a Medium of Instruction (EMI) in an international master’s program in Italy. Using a case study approach, it investigates how variations in speech rate, disfluencies, and verbal and non-verbal strategies affect teaching and learning. Data were collected from a video-recorded lecture, student feedback, and the lecturer’s reflections, with a focus on student perspectives. Findings reveal significant variations in speech rate, with faster rates during straightforward explanations and slower rates for complex content. Disfluencies were analyzed to differentiate between communication breakdowns and deliberate pauses used to engage students or allow them to process material. Gestures were examined as tools for facilitating understanding or compensating for challenges in lexical retrieval and technical explanations. While the study reinforces that both students and lecturers cooperate effectively to achieve communicative goals in English as a Lingua Franca in Academic (ELFA) contexts, it also emphasizes the need to further empower EMI lecturers by providing research-based evidence to improve specific areas of communication. Despite the lecturer’s technical expertise, rapid speech, lexical retrieval issues, and occasional misalignments between gestures and speech hindered student comprehension, particularly in STEM vocabulary. Methodologically, this research expands EMI studies by integrating Second Language Acquisition (SLA), Conversation Analysis (CA), and multimodal approaches. It emphasizes gestures as integral communicative tools rather than mere speech accessories, revealing challenges that may not be expressed verbally. The findings highlight the embodied nature of teaching and the critical role of gestures in either enhancing or hindering comprehension. This study calls for EMI-specific training programs to enhance communicative strategies, meeting the needs of international and domestic students in technical disciplines like engineering

Multi-functional electrocatalysts for CO2 reduction reaction: towards the modulation of selectivity

The electrocatalytic reduction of CO2 (CO2RR) is a captivating strategy for the conversion of CO2 into fuels, to realize a carbon neutral circular economy. In the recent years, research has focused on the development of new materials and technology capable of capturing and converting CO2 into useful products. The main problem of CO2RR is given by its poor selectivity, which can lead to the formation of numerous reaction products, to the detriment of efficiencies. For this reason, the design of new electrocatalysts that selectively and efficiently reduce CO2 is a fundamental step for the future exploitation of this technology. Here we present a new class of electrocatalysts, designed with a modular approach, namely, deriving from the combination of different building blocks in a single nanostructure. With this approach it is possible to obtain materials with an innovative design and new functionalities, where the interconnections between the various components are essential to obtain a highly selective and efficient reduction of CO2, thus opening up new possibilities in the design of optimized electrocatalytic materials. By combining the unique physic-chemical properties of carbon nanostructures (CNS) with nanocrystalline metal oxides (MO), we were able to modulate the selectivity of CO2RR, with the production of formic acid and syngas at low overpotentials. The CNS have not only the task of stabilizing the MO nanoparticles, but the creation of an optimal interface between two nanostructures is able to improve the catalytic activity of the active phase of the material. While the presence of oxygen atoms in the MO creates defects that accelerate the reaction kinetics and stabilize certain reaction intermediates, selecting the reaction pathway. Finally, a part was dedicated to the study of the experimental parameters influencing the CO2RR, with the aim of improving the experimental setup in order to obtain commercial catalytic performances

Blank space: demographic estimation data-sparse contexts

This thesis explores novel approaches for the estimation of demographic outcomes in contexts where data are limited. In the first part, we investigate the potential of an emerging non-traditional data source for demographic research: online genealogies. Harnessing FamiLinx, a big genealogical database with over 86 million observations, we show that the availability of accurate and non-missing demographic information in online genealogical data is selective. Our findings reveal that individuals with a non-missing value in a demographic variable are more likely to present non-missing data in the other demographic variables, and to be embedded in family networks, whose members exhibit demographic information of superior quality and completeness. In the second part, we develop a Bayesian method for estimating the total fertility rate (TFR) indirectly from defective data. By combining online genealogical data from FamiLinx populations with information from more reliable sources, the proposed method allows to obtain TFR estimates for seven European countries and the United States during the historical period 1751-1910, a time when many of these countries lacked well-functioning civil registration systems. In the third part, we build a Bayesian model for the estimation of subnational male and female TFRs. Using real data from the United States and simulated data from Australia, we demonstrate that the proposed method can produce reasonably accurate TFR estimates in contexts, such as small areas, where data tend to be sparse and highly variable. Throughout the second and third parts of this thesis, we leverage indirect estimation techniques within a flexible Bayesian modeling framework. This approach allows to incorporate multiple data sources, to capture regularities in demographic trends over time and space, and to account for uncertainty

Adaptable security in the cloud-to-thing continuum

Recent technological advancements have played a key role in seamlessly integrating cloud, edge, and Internet of Things (IoT) technologies, giving rise to the Cloud-to-Thing Continuum paradigm. This cloud model connects many heterogeneous resources that generate a large amount of data and collaborate to deliver next-generation services. While it has the potential to reshape several application domains, the number of connected entities remarkably broadens the security attack surface. One of the main problems is the lack of security measures to adapt to the dynamic and evolving conditions of the Cloud-To-Thing Continuum. To address this challenge, this dissertation proposes novel adaptable security mechanisms. Adaptable security is the capability of security controls, systems, and protocols to dynamically adjust to changing conditions and scenarios. However, since the design and development of novel security mechanisms can be explored from different perspectives and levels, we place our attention on threat modeling and access control. The contributions of the thesis can be summarized as follows. First, we introduce a model-based methodology that secures the design of edge and cyber-physical systems. This solution identifies threats, security controls, and moving target defense techniques based on system features. Then, we focus on access control management. Since access control policies are subject to modifications, we evaluate how they can be efficiently shared among distributed areas, highlighting the effectiveness of distributed ledger technologies. Furthermore, we propose a risk-based authorization middleware, adjusting permissions based on real-time data, and a federated learning framework that enhances trustworthiness by weighting each client's contributions according to the quality of their partial models. Finally, since authorization revocation is another critical concern, we present an efficient revocation scheme for verifiable credentials in IoT networks, featuring decentralization, demanding minimum storage and computing capabilities. All the mechanisms have been evaluated in different conditions, proving their adaptability to the Cloud-to-Thing Continuum landscape

Evaluation of the real nutritional value of processed food to improve their effect on health and increase their sustainability

Cured meats and dairy products are criticized for their salt content and synthetic additives. This has led to the development of strategies to reduce and replace these ingredients. Since the food matrix and technological processes can affect the bioaccessibility of nutrients, it is necessary to study their release during digestion to determine the real nutritional value of foods. In the first part of this PhD project, the impact on the nutritional quality of the reduction of sodium content and of the replacement of synthetic nitrates/nitrites with a combination of innovative formulations was evaluated in Parmigiano Reggiano Cheese and salami. For this purpose, an in vitro digestion model combined with different analytical techniques was used. The results showed that fatty acids and proteins release increased over time during digestion. At the end of digestion, the innovative formulation/processing did not negatively affect fatty acids release and protein hydrolysis, and led to the formation of bioactive peptides. The excessive intake of sugars is correlated with metabolic diseases. After the intestinal uptake, their release in the blood stream depends on their metabolic fate within the enterocyte. In the second part of this PhD project, the absorption and metabolism of glucose, fructose and sucrose was evaluated using intestinal cell line. A faster absorption of fructose than glucose was observed, and a different modulation of the synthesis/transport of other metabolites by monosaccharides was shown. Intestinal cells were also used to verify the stability and intestinal uptake of vitamins (A and D3) delivered to cells through two vehicles. It was shown that the presence of lipids protected the vitamin from external factors such as light, heat and oxygen, and improved their bioavailability Overall, the results obtained in this PhD project confirmed that considering only the chemical composition of foods is not sufficient to determine their nutritional value

Wire-arc additive manufacturing (WAAM): investigation of process dynamics, material insights, and application prospects

Wire-arc additive manufacturing (WAAM), more recently referred to as directed energy deposition-arc (DED-arc), is emerging as a cost-effective solution with high material deposition rate and lower lead time. Thus, making it one of the most desirable Additive Manufacturing (AM) process for large-scale fabrication. However, extensive research is needed to realise the benefits of this technology. This thesis comprehensively evaluates the performance and prospectives of WAAM processes. The first study investigated different metal transfer modes and their interactions with input variables. The second study explored various deposition strategies for test walls and cubic samples on their built structure and mechanical properties. In addition, this study shows the influence of layer-stacking in the building direction on microstructural evolution and material hardness. The third study was based on a detailed analysis of cored-wire in two different WAAM process variants i.e. GMA and PTA-based WAAM. A solid wire of same chemical composition was employed for the comparative study. Different shielding gases were also investigated for GMA process to witness their influence on the process outcome. Another study was followed by depositing a hardfacing cored-wire with the properties of corrosion resistance to investigate the WAAM process in melting and deposition of such wire consumables. PTA and GMA processes with different shielding gases were used for hardfacing cored-wire to explore the opportunities and challenges of depositing such hard-to-deposit materials. The last study covered a process-driven comparative study on wire-fed DED processes based on electric arc, plasma and laser sources such as GMA, PTA and LMD. Thus, the research provides important insights on WAAM, by exploring it from a process, product and application perspective