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Alternative strategies to improve intranasal and nose-to-brain drug delivery
Get PDFThe nasal route is an appealing option for drug administration due to features like increased patient compliance, avoidance of the first-pass effect, fast onset of action, and direct access to the central nervous system. Despite advantages, challenges include limited nasal tissue surface, mucus and epithelial barriers, as well as mucociliary clearance. This Ph.D. project aimed to overcome these shortcomings by investigating strategies capable of enhancing drug permeation through the nasal mucosa. Two main approaches were considered. One method involved adding molecular enhancers to increase membrane apparent permeability, specifically highlighting postbiotics as potential natural and eco-friendly excipients for nasal drug delivery. The other approach intended to counter mucociliary clearance using mucoadhesive agents and “smart” polymers, thus extending the residence time and favoring complete drug absorption. Parallel to this, the usefulness and applicability of different in vitro tools in the pre-clinical assessment of drug permeability were considered. Great attention was paid to the ready-to-use PermeaPad® biomimetic membrane, which was implemented with a layer of reconstituted mucin to mimic the composition of the nasal mucosa. This membrane was employed, together with tissue-based models and a primary cell-based system, to establish the ability and the mechanism of diffusion improvement by liquid and semisolid formulations
Serious Games for the communication of cultural memory: operational proposals in the urban regeneration context of the Darsena of Ravenna
Get PDFIl videogioco rappresenta un efficace strumento di apprendimento attivo grazie alla sua immersività e interattività. Il suo utilizzo in settori diversi dal puro intrattenimento è aumentato notevolmente, portando all’introduzione di una specifica terminologia. Il presente lavoro analizza le teorie ludiche e videoludiche, mettendo in evidenza il potenziale educativo e cognitivo del videogioco, con una digressione relativa all’importanza dell’Edutainment e dei Serious Games nel trasferimento di conoscenza e nella promozione culturale. La ricerca si focalizza sui Serious Games come strumenti di valorizzazione del patrimonio culturale, esaminando le metodologie in uso in ambiti museali e territoriali. Il contesto di riferimento della ricerca è il quartiere Darsena di Ravenna, uno spazio urbano significativo per lo sviluppo commerciale e industriale della città. Il progetto DARE-UIA (Digital Environment for collaborative Alliances to Regenerate urban Ecosystems), entro cui si sviluppa il presente lavoro, ha promosso la rigenerazione urbana e la valorizzazione della memoria culturale della Darsena attraverso strumenti di digital storytelling. L'obiettivo della presente ricerca è la progettazione di un Serious Game, denominato Delver, per sensibilizzare e coinvolgere i visitatori e la comunità locale nella scoperta del patrimonio culturale della Darsena. Attraverso una metodologia multidisciplinare, la tesi propone una serie di linee guida per la creazione di Serious Games, applicabili in contesti simili. Il videogioco Delver è presentato come un'avventura grafica 2D single player, arricchita da materiali multimediali provenienti da archivi storici e amatoriali degli abitanti stessi. L'obiettivo finale è dimostrare come i videogiochi possano essere efficaci strumenti di supporto per la riqualificazione urbana e la valorizzazione della memoria culturale locale. La tesi è strutturata in sette capitoli che coprono le teorie ludiche, l'evoluzione del videogioco, l'applicazione dei Serious Games nel patrimonio culturale e una proposta metodologica dettagliata per la creazione di un Serious Game specifico per la Darsena di Ravenna.Video games represent an effective tool for active learning thanks to their immersiveness and interactivity. Their use in sectors beyond pure entertainment has increased significantly, leading to the introduction of specific terminology. This work analyzes ludic and video game theories, highlighting the educational and cognitive potential of video games, with a digression on the importance of Edutainment and Serious Games in knowledge transfer and cultural promotion. The research focuses on Serious Games as tools for enhancing cultural heritage, examining methodologies used in museum and territorial contexts. The reference context of the research is the Darsena district of Ravenna, an urban space significant for the commercial and industrial development of the city. The DARE-UIA project (Digital Environment for collaborative Alliances to Regenerate urban Ecosystems), within which this work is developed, has promoted urban regeneration and the enhancement of the Darsena's cultural memory through digital storytelling tools. The objective of this research is to design a Serious Game, called Delver, to raise awareness and engage visitors and the local community in discovering the cultural heritage of the Darsena. Through a multidisciplinary methodology, the thesis proposes a series of guidelines for creating Serious Games, applicable in similar contexts. The Delver video game is presented as a single-player 2D graphic adventure, enriched with multimedia materials from historical archives and amateur collections of the inhabitants themselves. The ultimate goal is to demonstrate how video games can be effective support tools for urban regeneration and the enhancement of local cultural memory. The thesis is structured in seven chapters covering ludic theories, the evolution of video games, the application of Serious Games in cultural heritage, and a detailed methodological proposal for creating a Specific Serious Game for the Darsena of Ravenna
A multi-dimensional analysis of party competition: exploring the impact of multiple systemic crises on elections and on the process of government formation and termination in Southern Europe
Get PDFOver the last two decades, two major events have harshly hit Europe, especially southern European member states: the 2008 European sovereign debt crisis and the ongoing refugee crisis. This study attempts to investigate how the interplay of these critical events influenced party competition dynamics especially in those countries that have been heavily exposed not only to the negative consequences of the economic crisis but also to increasing migratory flows. The thesis is cumulative, being comprised of four manuscripts, each one addressing a specific facet of party competition. Manuscript I traces the evolution of the changing structure of national party systems, highlighting the tendency towards an increased dimensionality of political spaces. Manuscript II focuses on parties’ electoral strategies investigating the incentives that encourage political actors to engage in mobilization strategies over immigration vis-a-vis socio-economic issues. The third and fourth manuscripts focus on the process of government formation and termination, respectively. Manuscript III shows that, in a political context characterized by the intensified role exerted by European institutions in domestic affairs, political parties are more likely to engage in negotiations over coalition agreements with political actors sharing similar positions on the European integration process. Manuscript IV highlights an indirect mechanism through which exogenous non-economic shocks, such as the one represented by the refugee crisis, can undermine cabinet survival by means of deteriorating the inter-party bargaining environment within a government.
The dissertation aims to make a relevant contribution to the literature of party competition by advancing our comprehension on parties’ mutual interactions during critical circumstances. The study also contributes to the literature on government stability and demonstrates how parties’ ideological proximity on emerging salient issues enhances the likelihood that a given coalition will form and remain stable during the constitutional mandate
The objective of security in European Union law: nexus between the common foreign and security policy (CFSP) and and the area of freedom, security and justice (AFSJ)
Get PDFLa PESC et l’ELSJ sont deux domaines de compétences distincts poursuivant des objectifs de sécurité a priori distincts. Cette séparation de la sécurité ne résiste plus aux menaces sécuritaires actuelles revêtant des dimensions internes et externes. Ainsi, l’objectif de sécurité contribue au dépassement de cette séparation constitutionnelle entre les deux domaines et in fine à la structuration de leur articulation. La poursuite de l’objectif de sécurité implique des interactions multiples entre les instruments normatifs et opérationnels et requiert la mise en synergie entre les acteurs, tant institutionnels qu’opérationnels de la PESC et de l'ELSJ.CFSP and AFSJ are two distinct areas of competence pursuing a priori distinct security objectives. This division of security no longer resists to current security threats with internal and external dimensions. Thus, the objective of security contributes to the overcoming of this division between the two areas and ultimately to the structuring of their articulation. The pursuit of the security objective involves multiple interactions between the normatif and operational instruments and requires the establishment of synergies between the institutional and operational actor of CFSP and AFSJ
Machine learning for cultural heritage conservation: decoding the past through analysis of hyperspectral data
Get PDFCultural heritage conservation and restoration stand at the crossroads of art, history, science, and technological progress. Traditional conservation and restoration methods, while invaluable, are often constrained by their ability to adapt to the complexity of cultural artefacts. In the contemporary landscape, analytical instruments and computational technology are profoundly shaping the interdisciplinary field. Machine learning and data analysis, with their capacity to unravel intricate patterns and trends within extensive datasets, offer a promising avenue for enhancing conservation and restoration practices. This thesis aims to investigate the potential and limitations of machine-learning techniques in processing extensive hyperspectral data acquired from historical art objects. Through various case studies presented, we assess the effectiveness of machine learning models, from off-the-shelf techniques to self-developed algorithms, in supporting tasks ranging from material diagnostics and classification to mapping and digital restoration. Additionally, we critically evaluate the challenges and limitations associated with the implementation of machine learning, specifically constrained by CH, and explore the transferability of machine learning models to similar scenarios. Rooted in the obtained results, this thesis contributes to the ongoing dialogue on leveraging cutting-edge technologies to preserve and celebrate our diverse cultural heritage
Argumentation for legal reasoning: meta-models, technology and beyond
Get PDFThis thesis presents a comprehensive exploration of argumentation in the context of legal reasoning, bridging the gap between formal argumentation theory and its technological applications. Central to this work is the enhancement of the ASPIC+ framework, integrating structured meta-argumentation to address limitations in reasoning about rules, conflicts, and preferences, including the concept of the burden of persuasion. This advancement expands the framework’s applicability in legal reasoning and beyond.
A pivotal aspect of this research is the development of the arg2p framework, a robust and versatile environment integrating theoretical advancements in argumentation. The framework marks a significant stride in realising practical, logic-based environments for argumentation in intelligent systems, demonstrating a marked focus on user-friendliness and technical maturity, crucial for bridging theoretical innovation with functional application.
The thesis also delves into the realm of machine learning (ML), illustrating the integration of structured argumentation with automated machine learning (AutoML). This integration is aimed at enhancing the transparency and control in the development of ML systems by offering a symbolic interface for incorporating expertise in ML, exemplifying the convergence of traditional symbolic AI methods with data-driven ML approaches.
This work significantly contributes to argumentation theory and legal AI, providing a nuanced understanding of meta-argumentation and its practical applications. The enhancements to ASPIC+, coupled with the arg2p framework, present new avenues for legal analysis and decision-making. The integration with ML further highlights the potential of structured argumentation in contemporary AI, paving the way for more robust and ethically sound AI systems across various domains
IBISCO trial: immune and pathological response in breast cancer after preoperative irradiation with stereotactic technique and neoadjuvant chemotherapy
Get PDFINTRODUCTION
Pathological complete response (pCR) after neoadjuvant systemic therapy is a predictor of disease outcomes and survival in breast cancer (BC) aggressive subtypes and for this reason it has become the standard of care for most triple-negative and Her2-positive BC. However, pCR after neoadjuvant chemotherapy (NAC) is still difficult to obtain in Luminal B BC, leading to the need for new strategies development. In this setting, the IBISCO trial evaluates the combination of preoperative stereotactic radiotherapy (SBRT) boost and standard NAC to enhance pCR.
METHODS
IBISCO is a phase II trial that aims to demonstrate an increase in pCR rates from 15% to 35% in Luminal B BC patients by adding a preoperative SBRT boost to the standard scheme of NAC. The primary endpoint is evaluated with the Residual Cancer Burden index, and explorative analysis of the tumour-associated inflammatory microenvironment and tumour genomic profile modifications post-SBRT is conducted as well. Surgery and adjuvant therapies will be performed for clinical practice after the completion of neoadjuvant treatments.
RESULTS
Between February and February 2024, ten patients with Luminal B BC addressed to NAC were enrolled. The majority had cT2 tumours. The preoperative RT boost for all patients was planned between the third and fourth administration of paclitaxel. The SBRT treatments were delivered with Volumetric Arc Modulated Therapy (VMAT) technique with flattening filter free (FFF) to optimize target coverage and using Deep Inspiration Breath Hold to minimize organs-at-risk exposure to higher RT doses.
CONCLUSIONS
Preliminary IBISCO trial results suggest safe integration of preoperative SBRT boost with NAC for Luminal B BC. No acute skin toxicities were observed, affirming the potential of modern RT techniques for delivering precise and effective treatment. The final results of the study will hopefully lead to an optimized integration of RT and NAC in this specific setting
Robotic perception and manipulation of deformable linear objects
Get PDFDeformable objects are pervasive in the everyday life environment, in the form of clothes, cables, wires, ropes and many other objects. Despite their importance and widespread diffusion, there still exist many limitations when it comes to deploying robotic systems for interacting with deformable objects.
This thesis presents a comprehensive exploration of research activities geared towards enhancing the perception and manipulation capabilities of a robotic system when dealing with deformable linear objects.
The activities are organized into two main research aspects, namely perception and manipulation. In the first part of this thesis, the focus is on developing perception solutions for deformable linear objects, primarily relying on visual data and exploiting deep learning techniques. Consequently, innovative methods are developed to address the dataset generation challenge with minimal to no human intervention. Furthermore, novel approaches are applied to tackle the instance segmentation task by combining deep learning techniques with graph-based representations of the object's configuration. The 3D reconstruction task is also addressed through a multi-view stereo reconstruction approach.
The second aspect of the research concentrated on the manipulation problem, specifically in predicting how robot actions affect the deformable linear object's configuration. This is achieved by employing a differentiable model of the object's dynamics that is used for planning the optimal manipulation action for achieving a target configuration. The same model is also used for estimating model parameters, thereby improving the prediction accuracy and consequently enhancing the robotic system's manipulation capabilities.
Finally, the perception methods developed in this thesis are extended to encompass the perception of deformable multi-linear objects, such as wire harnesses. To this end, a learning-based topological representation is conceived and applied in the context of a dual-arm disentangling manipulation task
Energy-efficient time series analysis with machine learning and deep learning on embedded computing platforms
Get PDFThe present Ph.D. thesis presents techniques and solutions for energy-efficient time-series analysis based on automated learning executed on resource-constrained, low-power computing platforms, with an interest in both Deep Learning and traditional, non-deep Machine Learning. This dissertation spans diverse domains, from algorithmic research on the accuracy-efficiency tradeoff in processing different biosignals to applied research inspired by industrial scenarios.
The unifying methodology that brings all the research questions addressed in this thesis under the same perspective is the interest in time-series analysis as a task to be performed in the presence of the resource constraints characteristic of low-power edge computing devices.
This dissertation covers the three major types of automated learning tasks: binary classification, multi-class (single-label) classification, and regression. Starting from binary classification, this work presents a proximity sensor for active safety in industrial machinery and a setup for epilepsy detection from intracranial electroencephalography. Both solutions are based on a Temporal Convolutional Network (TCN) executed on an embedded MCU. Moving to multi-class (single-label) classification and regression, the research addresses hand modeling from the surface electromyographic (sEMG) signal. Starting with off-device TCNs for the recognition of discrete hand gestures, the classification setup is advanced via deployment on a multi-core MCU and heuristics for unsupervised adaptation to arm posture. Then, regression was addressed for a more versatile control of Human-Machine Interfaces (HMIs). Developing an embedded TCN accurate in hand kinematics estimation, I addressed the modeling of hand kinematics and force with event-based features, which are computationally cheaper and promising for future porting onto event-driven devices with reduced latency and energy consumption. The sEMG contributions advance the field of non-invasive intuitive wearable HMIs.
This research work proves the success of the embedded approach to time-series Machine Learning, achieving SoA accuracy and efficiency and proving promising for impactful applications in the industrial, clinical, and consumer domains
Electrodynamic analysis of HTS conductors for fusion magnets and for non insulated coils
Get PDFThe advent of high-temperature superconducting (HTS) materials has ushered in a new era of technological possibilities, propelling them to the forefront of cutting-edge scientific endeavors. HTS devices are frequently subjected to time dependent transport currents and external magnetic fields during operation, leading to energy losses within the HTS, namely AC losses, a phenomenon that can potentially compromise device performance. Accordingly, the development of robust tools for accurately estimating AC losses in HTS devices and predicting the behavior of the device during operation is of crucial importance. Conventional approaches, such as finite element method (FEM) models, offer commendable predictive capabilities, yet they suffer from substantial computational costs and are often not compatible with the time scale of the design and prototyping phases of HTS device development. The computational time of 3D FEM models is prohibitively high in the cases of complex magnet geometry like the central solenoid of a Tokamak fusion reactor. To address this limitation, this thesis investigates alternative solutions: analytical formulae for the assessment of the instantaneous power dissipation, which were applied to study the losses in the central solenoid of the DEMO machine, and a 3D lumped parameter model employed in the study of tapes and no insulation HTS (NI-HTS) coils. The proposed solutions, although not accurate as a full 3D FEM model which remains the most reliable tool for the analysis of HTS devices, offer significant advantages over conventional approaches. The main advantages are reduced computational time, enhanced model flexibility, and improved scalability for complex device geometries