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The physics of blazar jets in the context of multi-wavelength and multi-messenger astronomy
No full textBlazars, 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
Functional and structural optimization of electrospun scaffolds for the regeneration of ligaments and tendons
No full textTendons and ligaments are the connective tissues between muscles and bones that provide stability and support to joints. Current treatments for injuries to these tissues often have limited repair and, for the most part, do not recover full function, resulting in symptoms such as chronic pain, instability and impaired function. To address these limitations, electrospinning is a promising and versatile technique to produce nanofibrous scaffolds that mimic the natural extracellular matrix and biomechanical properties of tendons and ligaments. The specific objective of this research work was to optimise the structure and function of electrospun scaffolds for tendon and ligament regeneration. Two critical areas of scaffold optimisation are examined in this research work: the effects of gamma-ray sterilisation on scaffold properties and a novel approach to gradually mineralise an electrospun hierarchical bundle scaffold for enthesis regeneration. Gamma-ray radiations are one of the most suitable treatments to sterilise electrospun devices, but the amount of irradiation has to be carefully chosen to avoid polymeric nanofiber degradation and changes in biomaterial properties. The first study in the Thesis aimed to investigate the effects of gamma-irradiation on electrospun nanofibrous bundles of aligned nanofibers inspired by tendon/ligament fascicles which were irradiated at three different doses (5, 10, and 25 kGy).
The effects of the irradiation were investigated by analysing the scaffold’s morphology, physicochemical properties and mechanical properties to identify the most suitable range of radiation that least affected scaffolds’ structure and mechanics, while granting effective sterilisation. The second study was aimed at gradient mineralisation of the fascicle-inspired electrospun bundles for enthesis regeneration. An alternative soaking method was used to deposit minerals in a gradient fashion to mimic the mineral gradient found in native entheses. Morphological, physicochemical and mechanical characterisation of these mineralised bundles showed that they were ideal candidates for repair of tendon and ligament enthesis
Development of explainable and reproducible artificial intelligence for medicine
Get PDFArtificial intelligence (AI) holds the potential to revolutionize medicine and healthcare, especially in diagnosis and treatment. However, integrating AI into medicine presents several challenges that demand immediate consideration. This study examines three key aspects: explainability, reproducibility, and the scarcity of data due to privacy concerns. Explainability is vital for increasing trust in AI systems, especially in medical applications where decisions directly impact patient well-being. Reproducibility ensures the reliability of machine learning models across different settings. In this work, a new algorithm is proposed to compute average explanations to enhance these aspects. This approach aims to provide consistent and reproducible explanations, particularly in validation settings, contributing to the transparency and reliability of AI in medical decision-making. Additionally, privacy regulations intensify the scarcity of medical data, which prevents the development of effective AI models. In response, this investigation explores the potential of applying swarm learning (SL). Swarm learning is a recently proposed technology that empowers collaborative model training across decentralized data and computational sources while preserving data privacy. This innovative approach overcomes data scarcity issues and ensures compliance with stringent privacy regulations, preparing for a more robust AI development in the medical domain. This study underscores the necessity of addressing critical aspects such as explainability, reproducibility, and privacy concerns when deploying AI for healthcare applications
Accretion properties of supermassive black holes across cosmic time and luminosities
Get PDFThis thesis comprehensively explores Active Galactic Nuclei (AGN) since cosmic noon and down to the lowest luminosities, utilizing a multiwavelength approach spanning X-ray to radio wavelengths. A key contribution is a method leveraging narrowband data from the miniJPAS survey, enabling the characterization of AGN properties while constraining their host galaxies. Robust physical parameters for X-ray-selected AGN up to z∼2.5 are derived. The analysis includes the examination of accretion ratios, a comparison with their proxies, and a forward modeling approach for assessing coevolution scenarios relative to local relations.
Another significant contribution is a novel SED fitting module tailored for Low-Luminosity AGN (LLAGN), accurately characterizing their properties. Validated with diverse AGN samples, this module proves effective even in scenarios with galaxy contamination. The derived X-ray bolometric correction and exploration of UV-X-ray relations for LLAGN are presented. We further explore the host galaxy contamination problem and the possible impact of LLAGN on their host galaxies' specific star formation rate.
The thesis concludes with a case study on how an LLAGN influences the interstellar medium of its host galaxy. High-resolution observations of the spiral galaxy M58 unveil H2 emission from warm molecular gas, indicating suppressed star formation in the inner kiloparsecs. Dust molecules remain unaffected compared to more luminous AGN. Optical forbidden line ratios suggest heating by low-velocity shocks caused by a low-power radio jet, highlighting the significant impact of LLAGN feedback on their host galaxies
Crafting and developing alternative organizations: temporal work and ideology in social movements
Get PDFThis dissertation investigates the emergence and evolution of alternative organizational forms within social movement contexts. Alternative organizations arise in juxtaposition to dominant organizational models, embodying specific values and practices. Social movements play a critical role in creating and diffusing these alternative organizational forms, as they establish a shared system of meanings that legitimizes and promotes these new templates.
Two empirical studies aim to illuminate this process, particularly focusing on a key challenge faced by alternative organizations: their reliance on future expectations. The empirical analysis centers on the Platform Cooperativism movement, which seeks to promote democracy and equality in the digital economy through the establishment of alternative digital platforms. Data were collected from
transcripts of video recordings of the movement's conferences and analyzed using a combination of
computational and manual text analysis techniques.
Firstly, one study examines how social movements collectively craft these alternatives. This investigation reveals that actors engage in various forms of temporal work – the interpretation of past, present, and future scenarios – to shape both the perceived desirability and feasibility of alternative organizations. These efforts serve the double objective of mobilizing support for action and defining the characteristics of the proposed alternatives.
Secondly, the other study explores the evolution of the collective system of meanings that underpins the movement as the advocated alternatives begin to materialize. Findings illustrate that, at its inception, the movement had a solid ideological traction, aimed at defining its values, identity, and objectives. However, over time, this ideological emphasis gives way to a more practical orientation focused on grappling with the challenges of implementing the proposed alternative structures and
practices.
Overall, these insights enhance our understanding of the processes by which social movements develop alternative organizational structures, thus paving the way for further research at the intersection of social movements and organizational studies
Integrating variational and learning models for imaging inverse problems
Get PDFImaging inverse problems are fundamental in various fields like diagnostic medicine and manufacturing engineering. Current methods for reconstruction can be divided into variational and learning-based models. Variational techniques use knowledge of the acquisition model to to reformulate the inverse problem as an optimization problem. The performances of these approach is often limited by regularization choices. Learning-based methods learn reconstruction maps directly from the data but lack consistent theoretical understanding.
This thesis explores different integrated frameworks, specifically developed to overcome some limitations of both approaches, demonstrating improvements without sacrificing the performances. Variational models incorporating data-driven techniques are improved, considering a bilevel framework for Total Variation regularization or a Plug-and-Play convergent schemes for iterative reconstruction. Several deep learning architectures for image reconstruction are presented, including models for super resolution microscopy and few-view CT, as well as regularization strategies within the Deep Image Prior framework. These approaches highlight the importance of architecture choice and the potential for improvement by incorporating handcrafted regularization terms in deep learning framework. The proposed approaches show how the choice of the architecture in learning-based models is crucial. In addition, their general performances can be improved by employing handcrafted regularization terms, as in the variational framework.
In conclusion, the models presented in this thesis confirm that the tools, developed by regularization theory, represent an important component to analyze and control the theoretical guarantees and properties of learning-based techniques, when applied to imaging inverse problems
Enabling multi-tasking AI-based perception on autonomous nano-UAVS
Get PDFToday, artificial intelligence (AI) is rapidly advancing, enabling increasingly complex capabilities in tiny flying robots.
Tiny AI-driven unmanned aerial vehicles (UAVs) are envisioned to achieve intelligence akin to biological systems like insects. Bees, for example, can pursue multiple goals concurrently with full autonomy. Developing sophisticated skills in miniaturized UAVs holds the potential for wide-ranging applications, significantly impacting many aspects of our lives.
However, the miniaturization of UAVs presents several challenges. Nano-drones, approximately 10cm in diameter, are at the forefront but cannot yet execute multiple intelligence tasks concurrently due to limited size and payload, which restrict them to ultra-low-power (ULP) processors with stringent computational and memory constraints.
This thesis aims to narrow the intelligence gap between tiny flying robots and insects by enabling concurrent execution of multiple real-time AI-based perception tasks on autonomous nano-UAVs.
First, we present methodologies and software tools for automating and optimizing convolutional neural networks (CNNs) deployment on nano-UAVs, adhering to their ULP processor constraints, and we apply our methodology to a CNN for visual autonomous navigation.
Second, we minimize the CNN workload on nano-drones. We identify inactive neurons within the CNN and introduce architecture modifications to shrink the network.
Applying this methodology to a state-of-the-art visual-based autonomous navigation CNN, we achieve a network that is 50x smaller and 8.5x faster than the baseline without compromising performance.
Third, leveraging the freed computational resources, we enable nano-UAVs to perform multiple AI tasks in real-time by deploying a CNN for object detection alongside the visual-based navigation CNN.
Combining techniques for CNN optimization and automated deployment and integrating two CNNs on a ULP processor, we demonstrate the ability to overcome computational and memory constraints, allowing simultaneous execution of multiple AI-based perception tasks on nano-UAVs. This milestone brings tiny flying robots closer to the high-level intelligence and multi-tasking capabilities of biological systems
Dark sectors as an approach to BSM searches. From terrestrial experiments to astrophysical ones.
Get PDFNeutrino masses, dark matter and the baryon asymmetry of the Universe call for new physics beyond the Standard Model. In recent years, low scale extensions of the Standard Model, the so-called dark sectors, have received significant attention: they postulate the existence of new particles and interactions below the electroweak scale and they communicate with the visible sector via feebly couplings, called portals. The main strength of this approach is that we can get a rich phenomenology from just a few added dark parameters of the theory. Their existence is highly motivated by observed anomalies in a vast range of experiments. We study one particular dark model in detail, the Three Portal model, though we also consider other kinds of realizations of dark sectors. We first present the current status of dark sector parameters. Then, we explain the role of dark sectors in one particular neutrino anomaly: MiniBooNE's low energy excess. We particularly study how a dark photon and one or two heavy neutral leptons from the Three Portal model could explain the anomalous events in MiniBooNE and perform a fitting of the model. Furthermore, we consider this model in hot white dwarfs, so that constraints could be set on the parameter space of the dark photon
Spatiotemporal algorithms for measuring ecosystem heterogeneity from space
Get PDFAim: This thesis integrates new open-source algorithms for the monitor- ing of ecosystem heterogeneity through remote sensing. The project is or- ganized into three distinct parts, focusing on the measurement of spatial patterns, temporal trends, and spatiotemporal patterns. Methods: Due to its widespread use in ecological research, the algorithms presented in this thesis were developed using the R software. i) Chapter 1 introduces the functions included in rasterdiv package for the calculation of spatial heterogeneity. In Chapter 2 is described a new function (RaoAUC()) for the computation of spatial heterogeneity that summarizes the information of parametric Rao index in a single metric. ii) Chapter 3 introduces the helical graphs, a novel visualization method for temporal trends in biodiversity drivers, plotting the mean values of a variable calculated at various points in time against the corresponding rate of change of the selected variable. iii) Chapter 4 presents a new method to quantify and visualize spatiotemporal heterogeneity change of an area exploiting beta diversity measures. Results and Discussions: i) The metrics tested in Chapter 1 offer insights into various facets of spatial heterogeneity, integrating available information of Earth surface properties, including aspects of functional, taxonomic, phylogenetic and genetic diversity. The RaoAUC() function tested in Chapter 2, emerges as a valuable tool for identifying areas susceptible to environmental changes. ii) Chapter 3 proved that helical graphs efficiently highlight temporal trends of environmental variables and can be exploited in various applications. iii) The spatiotemporal maps developed in Chapter 4 are not only intuitive and easily interpretable but also provide a quantitative measure that seamlessly integrates into modeling frameworks. Conclusion: The algorithms presented in this thesis have proven their efficacy, interpretability, and versatility, contributing valuable insights into distinct aspects of ecosystem heterogeneity
Unfinished Nursing Care for the patient at risk and with Delirium
Get PDFBackground
Patients with delirium are defined in the literature as frailty patients who are more vulnerable to unfinished care due to their inability to communicate their needs. Unfinished nursing care (UNC) is used as an umbrella term, in the literature and is defined as 'a problem of time scarcity that leads nurses to implicitly ration care through the clinical prioritisation process. From this knowledge gap, a research project was built with the following objectives: a) To describe the state of the art about concepts used in the literature to describe the cognitive process underlying UNC; the conceptual models and the measurement tools available; b) To explore how nurses prioritise interventions in practice and the reasons for such choices within acute and post-acute settings for patients at risk of functional and/or cognitive decline at risk of delirium. c) To adapt the Unfinished Nursing Care Survey (UNCS) tool for the assessment of delayed or missed care among patients at risk or with delirium and to evaluate its psychometric properties in a sample of nurses.
Materials and methods
To meet objective a) a Scoping Review was conducted, b) a Q-Methodology study was conducted, (involving a systematic literature review and the Nominal Group Technique to produce the Q methodology materials). Finally, to meet objective c) a validation study was performed
Results
Prioritisation is an important activity for nurses, who have to decide which unfinished nursing tasks to prioritise. In study b) 56 nurses working in medical, geriatric and post-acute care facilities were involved. The results describe the preventive and management interventions that received the highest/lowest priority, the patterns that emerged among the nurses, and the reasons.
Conclusions
This project highlighted the importance of focusing on the way nurses prioritise, motivate and measure unfinished care to promote safe and quality care