Verso la "giustizia" delle piattaforme? Contributo allo studio della composizione extragiudiziale dei conflitti nei mercati digitali

Da alcuni decenni l'UE sta promuovendo l'uso di sistemi di risoluzione alternativa delle controversie (ADR) per favorire l'accesso alla giustizia dei consumatori. La presente tesi fornisce una panoramica completa della "prima generazione" di regole in tema di ADR, con l'obiettivo di indagare le ragioni strutturali del fallimento di tale cornice normativa nel colmare il divario con la pratica commerciale nella risoluzione delle controversie osservabile nei mercati digitali. L'emergere del modello organizzativo della piattaforma nei mercati digitali ha evidenziato l’urgenza di una nuova ondata di regolamentazione. In particolare, le piattaforme digitali di grandi dimensioni (VLOPs) si pongono nell’ottica di esercitare funzioni simili a quelle di aggiudicazione delle controversie precedentemente svolte, in maniera esclusiva, dai sistemi giuridici nazionali o dalle istituzioni ADR. La seconda parte della tesi si basa sull'analisi del fenomeno delle piattaforme digitali da una prospettiva di diritto civile, considerando l'evoluzione del diritto dell'UE in questo settore e il dibattito dottrinale sulle relazioni contrattuali nell’economia delle piattaforme. L'analisi si concentrerà sui sistemi interni di gestione dei reclami utilizzati dalle VLOPs per risolvere i propri conflitti con gli utenti o per giudicare controversie tra utenti. Questi sistemi saranno inquadrati come online dispute resolution (ODR) delle piattaforme. Per sostenere l'analisi del fenomeno, la tesi presenterà quattro casi studio di sistemi di ODR attualmente offerti da VLOPs di diverse categorie. Complessivamente, la tesi mira a fornire una nuova dimensione alla nozione di ODR, offrendo un dettagliato quadro del ruolo delle piattaforme digitali nella risoluzione delle controversie, anche alla luce del Regolamento Platform-to-business (UE 1150/2019) e del Digital Service Act (UE 2065/2022). Dall’indagine emerge la necessità per gli studiosi del diritto processuale civile di prestare attenzione a questo fenomeno emergente, anche al fine di evitare che la risoluzione delle controversie operata dalle piattaforme digitali diventi un ostacolo sostanziale all'accesso alla giustizia dei cittadini.

On the motion planning & control of nonlinear robotic systems

In the last decades, we saw a soaring interest in autonomous robots boosted not only by academia and industry, but also by the ever in- creasing demand from civil users. As a matter of fact, autonomous robots are fast spreading in all aspects of human life, we can see them clean houses, navigate through city traffic, or harvest fruits and vegetables. Almost all commercial drones already exhibit unprecedented and sophisticated skills which makes them suitable for these applications, such as obstacle avoidance, simultaneous localisation and mapping, path planning, visual-inertial odometry, and object tracking. The major limitations of such robotic platforms lie in the limited payload that can carry, in their costs, and in the limited autonomy due to finite battery capability. For this reason researchers start to develop new algorithms able to run even on resource constrained platforms both in terms of computation capabilities and limited types of endowed sensors, focusing especially on very cheap sensors and hardware. The possibility to use a limited number of sensors allowed to scale a lot the UAVs size, while the implementation of new efficient algorithms, performing the same task in lower time, allows for lower autonomy. However, the developed robots are not mature enough to completely operate autonomously without human supervision due to still too big dimensions (especially for aerial vehicles), which make these platforms unsafe for humans, and the high probability of numerical, and decision, errors that robots may make. In this perspective, this thesis aims to review and improve the current state-of-the-art solutions for autonomous navigation from a purely practical point of view. In particular, we deeply focused on the problems of robot control, trajectory planning, environments exploration, and obstacle avoidance.

Nutritional strategies to modulate metabolism and heat stress resilience in dairy cattle

Physiological and environmental stressors can disrupt barrier integrity at epithelial interfaces (e.g., uterine, mammary, intestinal, and lung), which are constantly exposed to pathogens that can lead to the activation of the immune system. Unresolved inflammation can result in the emergence of metabolic and infectious diseases. Maintaining cow health and performance during periods of immune activation such as in the peripartum or under heat stress represents a significant obstacle to the dairy industry. Feeding microencapsulated organic acids and pure botanicals (OAPB) has shown to improve intestinal health in monogastric species and prevent systemic inflammation via the gut-liver axis. Feeding unsaturated fatty acids (FA) such as oleic acid (OA) and very-long-chain omega-3 (VLC n-3) FA are of interest in dairy cow nutrition because of their potential to improve health, fertility, and milk production. In the first study, we evaluated the effects of heat stress (HS) conditions and dietary OAPB supplementation on gut permeability and milk production. In parallel with an improved milk performance and N metabolism, cows supplemented with OAPB also had an enhanced hepatic methyl donor status and greater inflammatory and oxidative stress status compared to the HS control group. In a second study, we evaluated the relative bioavailability of VLC n-3 in cows fed a bolus of rumen-protected (RP) fish oil (FO). In a third study, we proved the interaction between RPFO and RP choline to promote the synthesis of phosphatydilcholines. Lipid forms that support hepatic triglyceride export and can prevent steatosis in dairy cows. The last study, demonstrated that algae oil outperforms against a toxin challenge compared to FO and that feeding RPOA modulates energy partitioning relative to n-3 FA-containing oils. Overall, this thesis confirms the need and the effectiveness of different strategies that aimed to improve dairy cows’ health and performance under heat stress, inflammation or metabolic disease.

Protein-based nanomaterials as protein heterogeneous nucleants and structural studies on enzymes from two photosynthetic organisms

Proteins, the most essential biological macromolecules, are involved in nearly every aspect of life. The elucidation of their three-dimensional structures through X-ray analysis has significantly contributed to our understanding of fundamental mechanisms in life processes. However, the obstacle of obtaining high-resolution protein crystals remains significant. Thus, searching for materials that can effectively induce nucleation of crystals is a promising and active field. This thesis work characterizes and prepares albumin nanoparticles as heterogeneous nucleants for protein crystallization. These stable Bovine Serum Albumin nanoparticles were synthesized via the desolvation method, purified efficiently, and characterized in terms of dimension, morphology, and secondary structure. The ability of BSA-NPs to induce macromolecule nucleation was tested on three model proteins, exhibiting significant results, with larger NPs inducing more nucleation. The second part of this work focuses on the structural study, mainly through X-ray crystallography, of five chloroplast and cytosolic enzymes involved in the fundamental cellular processes of two photosynthetic organisms, Chlamydomonas reinhardtii and Arabidopsis thaliana. The structures of three enzymes involved in the Calvin-Benson-Bassham Cycle, phosphoribulokinase, troseposphatisomerase, and ribulosiophosphate epimerase from Chlamydomonas reinhardtii, were solved to investigate their catalytic and regulatory mechanisms. Additionally, the structure of nitrosylated-CrTPI made it possible to identify Cys14 as a target for nitrosylation, and the crystallographic structure of CrRPE was solved for the first time, providing insights into its catalytic and regulatory properties. Finally, the structure of S-nitrosoglutathione reductase, AtGSNOR, was compared with that of AtADH1, revealing differences in their catalytic sites. Overall, seven crystallographic structures, including partially oxidized CrPRK, CrPRK/ATP, CrPRK/ADP/Ru5P, CrTPI-nitrosylated, apo-CrRPE, apo-AtGSNOR, and AtADH1-NADH, were solved and are yet to be deposited in the PDB.

Big data and AI applications for health and research. Co-regulation mechanisms as a prosed solution for data protection law issues

Big data and AI are paving the way to promising scenarios in clinical practice and research. However, the use of such technologies might clash with GDPR requirements. Today, two forces are driving the EU policies in this domain. The first is the necessity to protect individuals’ safety and fundamental rights. The second is to incentivize the deployment of innovative technologies. The first objective is pursued by legislative acts such as the GDPR or the AIA, the second is supported by the new data strategy recently launched by the European Commission. Against this background, the thesis analyses the issue of GDPR compliance when big data and AI systems are implemented in the health domain. The thesis focuses on the use of co-regulatory tools for compliance with the GDPR. This work argues that there are two level of co-regulation in the EU legal system. The first, more general, is the approach pursued by the EU legislator when shaping legislative measures that deal with fast-evolving technologies. The GDPR can be deemed a co-regulatory solution since it mainly introduces general requirements, which implementation shall then be interpretated by the addressee of the law following a risk-based approach. This approach, although useful is costly and sometimes burdensome for organisations. The second co-regulatory level is represented by specific co-regulatory tools, such as code of conduct and certification mechanisms. These tools are meant to guide and support the interpretation effort of the addressee of the law. The thesis argues that the lack of co-regulatory tools which are supposed to implement data protection law in specific situations could be an obstacle to the deployment of innovative solutions in complex scenario such as the health ecosystem. The thesis advances hypothesis on theoretical level about the reasons of such a lack of co-regulatory solutions.

Directly training spiking neural networks for cyber-physical systems: from supervised to reinforcement learning

Spiking Neural Networks (SNNs) are bio-inspired Artificial Neural Networks (ANNs) utilizing discrete spiking signals, akin to neuron communication in the brain, making them ideal for real-time and energy-efficient Cyber-Physical Systems (CPSs). This thesis explores their potential in Structural Health Monitoring (SHM), leveraging low-cost MEMS accelerometers for early damage detection in motorway bridges. The study focuses on Long Short-Term SNNs (LSNNs), although their complex learning processes pose challenges. Comparing LSNNs with other ANN models and training algorithms for SHM, findings indicate LSNNs' effectiveness in damage identification, comparable to ANNs trained using traditional methods. Additionally, an optimized embedded LSNN implementation demonstrates a 54% reduction in execution time, but with longer pre-processing due to spike-based encoding. Furthermore, SNNs are applied in UAV obstacle avoidance, trained directly using a Reinforcement Learning (RL) algorithm with event-based input from a Dynamic Vision Sensor (DVS). Performance evaluation against Convolutional Neural Networks (CNNs) highlights SNNs' superior energy efficiency, showing a 6x decrease in energy consumption. The study also investigates embedded SNN implementations' latency and throughput in real-world deployments, emphasizing their potential for energy-efficient monitoring systems. This research contributes to advancing SHM and UAV obstacle avoidance through SNNs' efficient information processing and decision-making capabilities within CPS domains.