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Sticky diffusions on star graphs: Characterization and Itô formula
No full textIn this paper, we investigate continuous diffusions on star graphs with sticky behaviour at the vertex. These are Markov processes with continuous paths having a positive occupation time at the vertex. We characterize the sticky diffusions as time changed nonsticky diffusions by adapting the classical technique of Itô and McKean. We prove a form of Itô formula, also known as Freidlin–Sheu formula, for this type of process. As an intermediate step, we also obtain a stochastic differential equation satisfied by the radial component of the process. These results generalize those already known for sticky diffusions on a half-line and skew sticky diffusions on the real line
Positive Emotional Contagion and Neural Autonomic Resonance: from laughter paradigms to clinical insights
No full textThis doctoral thesis investigates how emotions are shared between people, focusing on positive emotional contagion (PEC) as a mechanism that promotes social connection and well-being. The research combines behavioural, physiological, and clinical approaches to explore how positive emotions are perceived and transmitted in both healthy individuals and stroke patients. The first chapter presents a review of the existing literature, showing that while emotional contagion has been widely studied for negative emotions, the positive dimension remains underexplored. The second chapter validates new Colour Analogue Scales for emotion and pain, demonstrating high reliability and practical efficiency in assessing affective states. The third chapter presents five studies examining empathic responses to emotional videos, revealing that both young and older adults recognise emotions accurately, with small age-related differences and subtle effects of laughter on positive resonance. The final chapter reports three clinical cases that bridge experimental research and clinical practice in neurorehabilitation. Together, these studies highlight the importance of positive emotional processes and their potential to support residual functional resources after brain injury. The thesis reflects a scientific journey moving from established theories to new perspectives for future research
Spazi interdisciplinari e pratiche sostenibili per l’analisi e la conservazione dei negativi del fondo Wilhelm von Gloeden - Fondazione Alinari per la Fotografia
No full textPhotographic objects display a heterogeneous and complex nature that calls for an interdisciplinary approach combining historical research, material analysis, and theoretical reflection. The study of Wilhelm von Gloeden’s photographic negatives preserved at the Alinari Foundation for Photography—hitherto unexamined—has made it possible to contribute to the philological reconstruction of the photographer’s artistic career, to reassess the originality of his vision, and to test a low-impact, non-invasive methodology for the study of photographic collections.
The research was structured in two main phases. The first involved a survey of both contemporary and modern sources, which yielded new biographical insights and identified iconographic connections extending from post-Romanticism to Symbolism, Pictorialism, Orientalism, and the Freikörperkultur movement, together with reflections on the reception and circulation of Gloeden’s photographic work. The second phase focused on the material analysis of negatives on glass, paper, and film, reconstructing the artist-photographer’s modus operandi and comparing it with practices described in the photographic literature of his time.
Finally, the study addressed the conservation issues of the collection, adopting a critical and sustainability-oriented approach to restoration. Non-invasive analysis—carried out through spectroscopic and imaging techniques on a selection of plates transported to the Opificio delle Pietre Dure in Florence for conservation—enabled the evaluation of both the potential and the limitations of such diagnostic methodologies when applied to historicized photographs
Improving photovoltaic power forecasting accuracy. A comparative study of hybrid models and PVGIS
No full textAccurate photovoltaic (PV) power forecasting is essential for ensuring reliable grid integration, particularly in regions with limited ground-based meteorological data. This study develops a hybrid multi-stage model chain designed for clear-sky PV power forecasting in Karaj, Iran, characterized by a cold semi-arid (BSk) climate. A 90 W polycrystalline module with a 27° tilt was evaluated using MERRA-2 atmospheric inputs and high-resolution on-site measurements for validation. Six global horizontal irradiance (GHI) models, five plane-of-array irradiance (POAI) models, and five module-temperature models were assessed. The Solcast, Perez, and PVsyst models demonstrated the highest accuracy within their respective stages. The combined model chain initially achieved an RMSE of 15.09 % and MBD of 5.12 %. After applying linear correlation adjustments, accuracy improved to an RMSE of 12.38 % and MBD of − 4.87 %. Comparative analysis showed that the optimized chain significantly outperformed the PVGIS-ERA5 and PVGIS-SARAH3 datasets, which exhibited RMSE values exceeding 85 % for the test location. The results highlight the importance of step-by-step validation and model selection under site-specific atmospheric conditions. The proposed framework offers a practical baseline for PV power prediction in semi-arid climates and may be extended in future work through machine-learning integration and multi-climate evaluation
Deep learning recognition and analysis of volatile organic compounds based on experimental and synthetic infrared absorption spectra
No full textVolatile Organic Compounds (VOCs) are organic molecules that have low boiling points and therefore easily evaporate in the air. They pose significant risks to human health, making their accurate detection crucial for efforts to monitor and minimize exposure. Infrared (IR) spectroscopy enables the ultrasensitive detection of VOCs at low-concentrations in the atmosphere by measuring their IR absorption spectra. However, the complexity of the IR spectra limits the possibility to implement VOC recognition and quantification in real-time. While deep neural networks (NNs) are increasingly used for the recognition of complex data structures, they typically require massive datasets for the training phase. Here, we create an experimental VOC dataset for nine different classes of compounds at various concentrations, using their IR absorption spectra. To further increase the amount of spectra and their diversity in terms of VOC concentration, we augment the experimental dataset with synthetic spectra created via conditional generative NNs. This approach allows us to train robust discriminative NNs, able to reliably identify the nine VOCs, as well as to precisely predict their concentrations. The trained NN is suitable for integration into sensing devices for VOCs recognition and analysis
Review of: D. Carraz, Templiers et Hospitaliers en France méridionale (1100-1300), in «Revue d’Études médiévales et de philologie romane», 5 (2026), p. 268-275
No full textReview of: D. Carraz, Templiers et Hospitaliers en France méridionale (1100-1300), in «Revue d’Études médiévales et de philologie romane», 5 (2026), p. 268-27
Comparison of Pragmatic Post-Thrombectomy Prognostic Scores Not Based on Advanced Imaging in a Large National Stroke Registry
No full textBackground: Nearly half of the patients who received endovascular thrombectomy (EVT) for large vessel occlusion experience poor functional outcomes. Reliable tools for early post-procedural prognostication are needed. We aimed to assess and compare the performance of existing, pragmatic post-EVT prognostic scores in a large national multicenter cohort. Methods: We conducted a systematic literature search to identify pragmatic post-thrombectomy prognostic scores predicting 90-day functional outcomes. Models relying on advanced imaging, small derivation samples, or machine learning were excluded. We analyzed data from the IRETAS registry-a prospective, multicenter Italian cohort of stroke patients treated with EVT. Inclusion criteria were pre-stroke modified Rankin Scale (mRS)≤2 and available 90-day mRS. The primary outcome was good functional outcome (mRS≤2). Prognostic performance was assessed using c-statistics in the samples where each individual score was measurable. Scores were compared using DeLong tests in the subset of patients for whom all scores were measurable. Results: Three scores were identified: HERMES-24, BET, and SNARL. Among 22768 patients in the registry, 18408 (89.1%) had a measurable HERMES-24 score, 13593 (59.7%) had a measurable BET score, and 19007 (83.5%) had a measurable SNARL score. Median age was 75 years (IQR 65-82), and 11528 (50.6%) were female. In the subset in which each test was measurable, HERMES-24 showed the best performance for predicting mRS≤2 (c-statistic=0.889), followed by BET (c-statistic=0.794) and SNARL (c-statistic=0.762) (p<0.001). In the subset of 12233 patients for whom all three prognostic scores were calculable, a head-to-head comparison confirmed the superior performance of the HERMES-24 model: HERMES-24 score vs. BET score (c-statistic difference=0.098 [95%CI=0.092-0.105]; p<0.001) and HERMES-24 score vs. SNARL score (c-statistic difference=0.124 [95%CI=0.116-0.132]; p<0.001)Conclusions:In this large, multicenter, national cohort, the post-EVT HERMES-24 score-which accounts only for age and 24-hour NIHSS-demonstrated the highest prognostic performance among existing, pragmatic post-EVT scores. Its simplicity and robust performance support its routine adoption in clinical practice
Maximum principle for higher order elliptic operators with inertia in general domains and any dimension
No full textIt is well known how the Maximum Principle (MP) in general fails to hold for uniformly elliptic operators of order higher than two, even in smooth convex domains. In D. Cassani and A. Tarsia (2022) it was shown in dimension N = 2, 3, by establishing a new Harnack type inequality, that the validity of the positivity preserving property can be restored when lower order derivatives are taken into account as a perturbation of the higher order differential operator. The restriction to the dimension was due to regularity issues which we develop here, extending the validity of the MP to any dimension and fairly general domains. Moreover, we show that the presence of inertial terms affects the range of the perturbation parameter, providing a balance between the positivity restoring effect of lower order derivatives and the mass energy. The method provided here is flexible with respect to the form of differential operators involved and thus suitable to be further extended to other classes of operators than just elliptic
An effective multi-revolution Lambert solver based on elementary calculus
No full textMulti-revolution Lambert solvers are intended to find the elliptic transfer orbits that are traveled multiple times and connect two specified positions in prescribed time, under the assumption of considering natural (Keplerian) orbital motion in the presence of a single attracting body. This study proposes and tests a new, effective multi-revolution Lambert solver that employs the initial true anomaly, which identifies the initial position along the transfer ellipse, as the unknown variable. The related search interval is identified through closed-form expressions for upper and lower bounds. A simple numerical algorithm is developed and employed over the entire search interval to detect all Lambert solutions. The new multi-revolution solver proposed in this work is simple to understand and easy to implement and is successfully tested in several challenging scenarios (corresponding to some pathological cases reported in the recent scientific literature), as well as for the study of Earth–Mars interplanetary transfers. Comparison with alternative, up-to-date techniques points out that the new approach at hand is able to detect all the feasible transfer ellipses, in all cases, with very satisfactory accuracy in terms of final position error, even in challenging scenarios that include a huge number of revolutions or near-antipodal terminal positions
Understanding and modeling technological conventional superconductors using ab initio quantum-mechanical methods
No full textThis thesis provides the first fully \textit{ab initio} description of the two workhorse superconductors in large-scale applications, NbTi and NbSn. Despite their technological relevance, these two materials had never been comprehensively described by first principles methods. In fact, in these cases, the underlying assumption of calculations based on the Migdal-Eliashberg theory -- dynamically stable structures -- breaks down in the standard harmonic approximation.
In this thesis, we address this issue by including anharmonic effects in the lattice dynamics through the Stochastic Self-Consistent Harmonic Approximation (SSCHA). This is achieved by implementing a novel approach that integrates the existing code with Machine Learning Interatomic Potentials (MLIP), thereby overcoming the computational workload of SSCHA, which would have made the calculations infeasible.
The methodology was first tested on NbTi due to its apparent simplicity. We demonstrate that the dynamical instabilities predicted at the harmonic level are eliminated if lattice dynamics is treated in an anharmonic framework, enabling the first \textit{ab initio} study of the superconducting properties of this material, over 60 years after its discovery. However, including anharmonic effects alone is not sufficient to reconcile predictions with experiments: we show that one must include additional, often-disregarded effects -- such as finite-bandwidth corrections, energy-dependent Coulomb interactions, and lattice disorder -- to obtain an accurate prediction of the superconducting . Our idea of thermal averaging over supercells to model disorder is one of the first attempts to include this effect in electron-phonon calculations.
After validation on NbTi, the SSCHA-MLIP workflow is applied to NbSn. This material is currently under optimization for adoption in medical devices and large international projects, such as the ITER fusion reactor and upgrades to the Large Hadron Collider at CERN. To this end, we offer a reliable theoretical foundation for understanding and improving its performance. This is achieved by addressing several questions that are crucial to material optimization and have been debated for over 70 years. In particular, we provide a microscopic explanation for the reduction in the upper critical field observed in samples that undergo the distinctive martensitic transition of A15 superconductors. The explanation stems from a simple, yet original, way of including Fermi surface anisotropy and strong-coupling effects in the evaluation of . Our treatment of the Fermi surface anisotropy provides a clear picture of how different doping strategies affect superconducting performance, suggesting practical strategies for optimization.
As in the case of NbTi, an accurate description of NbSn has required overcoming several limitations of standard \textit{ab initio} methods. Effects such as anharmonicity, energy-dependent Coulomb interactions, anisotropy, and disorder appear to be general features of technologically relevant superconductors. This thesis contributes to the community's efforts to extend the capabilities of first principles methods to describe real-world materials and to guide their optimization