Valorisation of tomato peel waste for lycopene encapsulation: Optimization and comparison of two green techniques

Lycopene, a lipid-soluble carotenoid with potent antioxidant properties, is typically found in tomato peels, which are often discarded as by-products in the food industry. This study focused on extracting lycopene using solvent extraction and encapsulating it in polycaprolactone (PCL), a biodegradable polymer, using two different methods: solvent evaporation and supercritical emulsion extraction (SEE). Both methods were used to produce microparticles for nutraceutical applications. An optimization study based on Box-Behnken design and response surface modelling was conducted to assess the effects of emulsification stirring speed, emulsification time, and polymer amount on encapsulation efficiency and particle size. Particle sizes, measured by laser diffraction, ranged between 1.77 ± 0.10 and 2.82 ± 0.17 μm for solvent evaporation, and between 1.12 ± 0.03 and 2.72 ± 0.15 μm for SEE. Encapsulation efficiencies, measured by UV–vis spectroscopy, ranged between 28.45 ± 0.28 % and 89.94 ± 1.70 % for solvent evaporation, and between 66.52 ± 0.64 % and 89.45 ± 1.31 % for SEE. Results show that SEE yields more consistent encapsulation efficiencies compared to solvent evaporation. Additionally, the design of experiments (DoE) approach helped identify optimal conditions that minimize waste and maximize productivity. This work offers a sustainable method for converting agro-industrial waste into valuable nutraceutical products

BEWT: A Benchmark for End-to-End Web Testing

Web applications are critical to modern life and require rigorous End-to-End (E2E) testing to ensure reliability across front-end and back-end components. While recent work has improved E2E testing—reducing cost, flakiness, and increasing robustness—a common benchmark is missing, hindering fair comparison and progress. This work introduces the first E2E benchmark dataset to address that gap: 12 Selenium WebDriver test suites for 8 web applications, packaged in Docker for easy deployment. It supports test evolution, automation, and flakiness studies, offering 389 Gherkin-based test cases, 283 Page Objects, 1,364 locators, and over 19k lines of code. By providing a reproducible, diverse foundation, this benchmark enables consistent evaluation of testing techniques and fosters advancement in E2E testing research

New Approaches of eXplainable AI: From Video Analytics to Federated Learning

Questa tesi indaga come l’eXplainable Artificial Intelligence (XAI) possa essere sistematicamente incorporata nelle pipeline di Machine Learning (ML) per applicazioni in scenari safety–critical. L’argomento centrale è che l’explainability non debba essere trattata come una funzionalità post-hoc, ma come un principio di progettazione che guida la costruzione, il monitoraggio e il dispiegamento dei sistemi. Il lavoro si sviluppa in due domini: la video analytics per la mobilità autonoma e assistiva, e l’apprendimento distribuito in presenza di vincoli di privacy e proprietà dei dati. Nel contesto della video analytics, sono state definite solide basi addestrando e ottimizzando YOLOv8s per ambienti indoor, con elevate prestazioni nel rilevamento di persone e sedie a rotelle. Su questo backbone è stato sviluppato un Operational Design Domain (ODD) Checker, che combina l’analisi delle feature visive con regole basate su alberi di decisione (DT) per un monitoraggio interpretabile e verificabile della sicurezza. L’explainability è stata poi estesa al ragionamento a livello di scena tramite una valutazione comparativa di modelli Vision–Language (CLIP, MiniGPT-4, GPT-4V), capaci di classificare ambienti di navigazione come “Safe to Proceed” o “Risky to Proceed” e contestualizzati nel paradigma dei cigni per la gestione dei rischi rari. Per quantificare l’affidabilità predittiva, la Conformal Prediction (CP) è stata applicata al rilevamento di oggetti, fornendo garanzie statistiche finite e mettendo in luce i compromessi tra strategie di quantificazione dell’incertezza (Uncertainty Quantification, UQ) box-wise e image-wise. Nel dominio dell’apprendimento distribuito, la tesi introduce Federated Learning with Interpretable Rule Transfer (FL-IRT), un framework che sostituisce l’aggregazione opaca dei parametri con la costruzione di modelli basati su regole sia lato client che lato server. FL-IRT consente di ottenere modelli globali competitivi in termini di accuratezza ma anche trasparenti nel processo decisionale, supportando al contempo meccanismi di aggregazione sicura e conformità con la normativa GDPR. Esperimenti condotti su diversi dataset confermano la sua scalabilità, la robustezza in condizioni non-iid e un notevole miglioramento di efficienza rispetto ai baseline neurali. Complessivamente, questi contributi dimostrano che l’explainability può essere integrata a diversi livelli di astrazione—dalle feature pixel–level e dal rilevamento di oggetti, fino al ragionamento semantico, alla calibrazione statistica e all’apprendimento distribuito. Avanzando la video analytics interpretabile, la quantificazione dell’incertezza basata su principi formali e i framework federati trasparenti, la tesi mostra che l’AI trustworthy-by-design è realizzabile senza sacrifici proibitivi in termini di accuratezza o efficienza. L’implicazione più ampia è che la XAI funge da livello regolatorio nell’AI, trasformando principi astratti di responsabilità e sicurezza in standard ingegneristici applicabili.This thesis investigates how eXplainable Artificial Intelligence (XAI) can be systematically embedded into Machine Learning (ML) pipelines for safety–critical applications. The central argument is that explainability should not be treated as a post-hoc feature but as a design principle that governs how systems are constructed, monitored, and deployed. The work spans two domains: video analytics for autonomous and assistive mobility, and distributed learning under privacy and ownership constraints. On the video analytics side, strong baselines were established by fine-tuning YOLOv8s for indoor mobility, achieving high accuracy in detecting people and wheelchairs. Building on this backbone, an Operational Design Domain (ODD) Checker was introduced, combining visual feature analysis with Decision Tree (DT) rules to provide interpretable, auditable safety monitoring. Explainability was extended to scene-level reasoning through benchmarking of Vision–Language Models (CLIP, MiniGPT-4, GPT-4V), which were evaluated on their ability to classify navigation scenes as “Safe to Proceed” or “Risky to Proceed” and contextualized within the swan metaphor for rare risks. To quantify predictive reliability, Conformal Prediction (CP) was applied to object detection, establishing finite-sample coverage guarantees and demonstrating the trade-offs between box-wise and image-wise Uncertainty Quantification (UQ) strategies. In distributed learning, the thesis introduces Federated Learning with Interpretable Rule Transfer (FL-IRT), a framework that replaces opaque parameter averaging with the construction of rule-based models at client and server levels. FL-IRT enables global models that are both competitive in accuracy and transparent in logic, while supporting secure aggregation and GDPR-compliant privacy mechanisms. Experimental results across multiple datasets confirm its scalability, robustness to non-iid conditions, and significant efficiency gains over neural baselines. Taken together, these contributions show that explainability can be embedded across abstraction layers—from pixel-level features and object detection to semantic reasoning, statistical calibration, and distributed learning. By advancing interpretable video analytics, principled UQ, and transparent federated frameworks, the thesis demonstrates that trustworthy-by-design AI is achievable without prohibitive sacrifices in accuracy or efficiency. The broader implication is that XAI functions as a regulatory layer in AI, transforming abstract principles of accountability and safety into enforceable engineering standards

Dynamic model and performance assessment of the natural motion of a SCARA-like manipulator in pick-and-place tasks

The energy efficiency of manipulators performing cyclic motions can be enhanced by utilizing the so-called natural motion, namely, the natural oscillations that occur when elastic elements are placed in series or parallel with the actuators. In this paper, the natural motion of the RR-4R-R robot is discussed. This manipulator exhibits a 4-DOF mobility similar to that of the widespread SCARA robot, but the vertical prismatic joint is replaced by a four-bar mechanism. This modification, along with the adoption of a direct-drive actuator for the four-bar mechanism, makes it easier to achieve the elastic balancing of the robot, allowing the exploitation of its natural motion. The robot dynamics is analysed using the Lagrangian approach. Two types of elastic balancing are considered: one using a torsional spring and one using a linear coil spring. A simplified model of the vertical motion is then proposed, decoupled from the inertial effects of the horizontal motion, and used to estimate the vertical natural period. The behaviour of the manipulator with natural elastic balancing is compared with that obtained with exact elastic balancing, which provides an indifferent equilibrium in any robot position. This comparison is first carried out in the time domain, and then the space of the robot operating conditions is sampled through multibody simulations, performed to investigate the threshold of convenience between exact and natural balancing. Simulation results indicate that exploiting the natural motion of the RR-4R-R manipulator can significantly reduce energy consumption in a wide range of industrial applications involving pick-and-place tasks

Growth performance, lipid metabolism, gut histoarchitecture and immune and antioxidant related gene expression in juvenile Asian sea bass, Lates calcarifer fed peroxidized lipids with or without dietary selenium nanoparticles

This study evaluated the effects of dietary recovered frying soybean oil (RFSBO) and selenium nanoparticles (SeNPs) on growth performance, hepatic metabolism, intestinal morphology, and the expression of antioxidant, immune, and growth-related genes in juvenile Asian sea bass (Lates calcarifer, 41.5 ± 0.1 g) reared under high temperature (32–33 °C) and high salinity (38–40 ppt). Six diets were formulated: fresh soybean oil (FSBO), FSBO + SN (4 mg/kg SeNPs), 50 % RFSBO, 50 % RFSBO + SN, 100 % RFSBO, and 100 % RFSBO + SN. Fish (n = 450) were randomly assigned to 18 tanks and fed to apparent satiation three times daily for eight weeks. Fish fed 50 % RFSBO + SN achieved similar final weights to the FSBO group but with significantly better feed conversion ratio, improved gut wall, epithelial, and villus height, and lower malic enzyme activity, indicating reduced metabolic stress. Hepatic triglycerides were significantly lower in this group than in FSBO-fed fish, while glycogen content was maintained. In contrast, 100 % RFSBO caused histological damage, oxidative stress, elevated isocitrate dehydrogenase activity, and lipid imbalance, with SeNPs offering only partial mitigation. SeNP supplementation upregulated gpx1, lyz, il-1β, and igf1 expression under moderate oxidative stress but had limited effects under severe conditions. Overall, RFSBO can replace up to 50 % of dietary FSBO without compromising growth or intestinal health when combined with SeNPs, but higher levels reduce SeNP efficacy. These findings support the use of moderate RFSBO inclusion with SeNP supplementation to sustain fish health and performance under challenging environmental conditions

A Branch & Bound Algorithm for the Rainbow Spanning Forest Problem

In this paper, we present a Branch & Bound algorithm for solving an NP-hard combinatorial optimisation problem denoted as Rainbow Spanning Forest Problem (RSFP). The RSFP consists of finding a spanning forest of an undirected, edge-coloured graph G with the minimum number of rainbow components, where a rainbow component of G is a subgraph of G that has all the edges with different colours. The proposed method is capable of determining the optimal solution within 0.4 s for small instances, thus proving to be computationally competitive with respect to the methods used by the most recent optimisation libraries and commercial SW environments available

Associations of positive end-expiratory pressure (PEEP) with extubation failure and clinical outcomes in invasively ventilated patients with acute brain injury: A secondary analysis of the ENIO study

Background: Invasive mechanical ventilation (IMV) is crucial for managing acute brain injury (ABI) patients, yet the effects of positive end-expiratory pressure (PEEP) on outcomes are not well understood. This study aimed to evaluate the relationship between PEEP levels and risk of extubation failure as well as intensive care unit (ICU) mortality in ABI patients. Methods: This post-hoc analysis of the ENIO study included 1512 ABI patients from the ENIO cohort, excluding those without available data on PEEP at day 1 and who never received an extubation trial. PEEP levels were recorded at days 1, 3, 7, and on the day of extubation. Logistic regression assessed the association between PEEP and extubation failure, while Cox proportional hazards regression analyzed ICU mortality. Results: Among 1154 included patients, extubation failure occurred in 21.2 % and ICU mortality was 3.7 %. Higher median PEEP at days 1, 3, and 7 was independently associated with increased odds ratio (OR) of extubation failure (OR = 1.13; 95 %CI = 1.01-1.26; p = 0.0294). At the time of extubation, higher PEEP was also significantly associated with extubation failure (OR = 1.13; 95 %CI = 1.02-1.25; p = 0.0218) and ICU mortality (Hazard Ratio, HR = 1.38; 95 %CI = 1.12-1.69; p = 0.0026). However, at sensitivity analyses adjusted for acute respiratory distress syndrome (ARDS), PEEP was no longer significantly associated with outcomes, while ARDS itself was an independent predictor of extubation failure. Conclusions: Extubating ABI patients at higher PEEP levels was associated with an increased risk of extubation failure and ICU mortality. However, this association likely reflects underlying respiratory pathology or disease severity. Our findings suggest that PEEP level may serve as a surrogate marker for extubation readiness, rather than a modifiable risk factor, and highlight the need for individualized assessment prior to extubation

On the robustness of adversarial training against uncertainty attacks

In learning problems, the noise inherent to the task at hand hinders the possibility to infer without a certain degree of uncertainty. Quantifying this uncertainty, regardless of its wide use, assumes high relevance for security-sensitive applications. Within these scenarios, it becomes fundamental to guarantee good (i.e., trustworthy) uncertainty measures, which downstream modules can securely employ to drive the final decision-making process. However, an attacker may be interested in forcing the system to produce either (i) highly uncertain outputs jeopardizing the system's availability or (ii) low uncertainty estimates, making the system accept uncertain samples that would instead require a careful inspection (e.g., human intervention). Therefore, it becomes fundamental to understand how to obtain robust uncertainty estimates against these kinds of attacks. In this work, we reveal both empirically and theoretically that defending against adversarial examples, i.e., carefully perturbed samples that cause misclassification, additionally guarantees a more secure, trustworthy uncertainty estimate under common attack scenarios without the need for an ad-hoc defense strategy. To support our claims, we evaluate multiple adversarial-robust classification models from the publicly available benchmark RobustBench on the CIFAR-10 and ImageNet datasets, and on a robust semantic segmentation model evaluated on Pascal-VOC. The code for the reproducibility of the experiments is available at the following link: https://github.com/pralab/UncertaintyAdversarialRobustness