Higher Institute on Territorial Systems for Innovation
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Particle Engineering via Spray Freeze-Drying for Pulmonary Drug Delivery Applications
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
Optical Systems for Advanced Sensing Application. From environment to cultural heritage monitoring.
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
Soil Displacement Estimation from Integrated Sensing Technologies in Data-Driven Models Biased by Temporal Coherence of PS-InSAR
No full textSpaceborne Synthetic Aperture Radar (SAR) interferometry provides long-term displacement measurements, but the quality of Persistent Scatterer (PS) time series depends critically on temporal coherence. Low-coherence points often exhibit auto-uncorrelated behaviours, which may be relevant to discriminate fast phenomena. This work introduces a coherence-based framework that identifies the coherence threshold beyond which PS displacement series retain sufficient reliability to support modelling. The threshold is estimated by analysing how data uncertainty, inferred through Sparse Bayesian Learning (SBL) techniques, varies with coherence and by detecting abrupt changes in this relationship. Once the optimal threshold is established, only the most reliable PS are used to train an SBL regression model linking satellite line-of-sight displacement to soil temperature and surface humidity measured by a low-cost ground sensor. PS-Interferometric SAR (PS-InSAR) time series are derived from COSMO-SkyMed raw images. The SBL model employs compressive-sensing principles and latent-parameter dictionaries of basis functions, whose latent parameters are calibrated through a constrained multi-start optimisation of a normalised residual-based objective function, regularised by a sub-validation dataset. In this work, it is shown that the trained model enables temporally denser reconstruction of displacement histories than the satellite revisit cycle allows and enables continuous soil monitoring by comparing model predictions with newly acquired PS-InSAR data
Extinction rates for nonradial solutions to the Stefan problem
Get PDFWe consider the one-phase Stefan problem describing the evolution of melting ice. On the one hand, we focus on understanding the evolution
of the free boundary near isolated singular points, and we establish for the
first time upper and (more surprisingly) lower estimates for its evolution. In
2D, these bounds almost match the best known ones for radial solutions, but
hold for all solutions to the Stefan problem, with no extra assumption on the
initial or boundary data.
On the other hand, as a consequence of our results, we also characterize the
global regularity of the free boundary, as follows: it can be written as a graph
{t = Γ(x)}, where Γ is C1 (and not C2) near any singular points in the lower
strata Σm, m ≤ n − 2. Moreover, Γ is not C1 at singular points in Σn−1
Measurement quality in data products for meteorology and climate
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
Novel protocols for entanglement characterization and protection
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
Circular Water Resources Integrating Sustainability and Innovation in Wastewater and Water Management
Get PDFAnalysis of Technological Readiness Indexes for Offshore Renewable Energies in Ibero-American Countries
Get PDFThe energy transition in Ibero-American countries demands significant diversification, yet the vast potential of offshore renewable energies (ORE) remains largely untapped. Slow adoption is often attributed to the hostile marine environment, high investment costs, and a lack of institutional, regulatory, and industrial readiness. A critical barrier for policymakers is the absence of methodologically robust tools to assess national preparedness. Existing indices typically rely on simplistic weighting schemes or are susceptible to known flaws, such as the rank reversal phenomenon, which undermines their credibility for strategic decision-making. This study addresses this gap by developing a multi-criteria decision-making (MCDM) framework based on a problem-specific synthesis of established optimization principles to construct a comprehensive Offshore Readiness Index (ORI) for 13 Ibero-American countries. The framework moves beyond traditional methods by employing an advanced weight-elicitation model rooted in the Robust Ordinal Regression (ROR) paradigm to analyze 42 sub-criteria across five domains: Regulation, Planning, Resource, Industry, and Grid. Its methodological core is a non-linear objective function that synergistically combines a Shannon entropy term to promote a maximally unbiased weight distribution and to prevent criterion exclusion, with an epistemic regularization penalty that anchors the solution to expert-derived priorities within each domain. The model is guided by high-level hierarchical constraints that reflect overarching policy assumptions, such as the primacy of Regulation and Planning, thereby ensuring strategic alignment. The resulting ORI ranks Spain first, followed by Mexico and Costa Rica. Spain’s leadership is underpinned by its exceptional performance in key domains, supported by specific enablers, such as a dedicated renewable energy roadmap. The optimized block weights validate the model’s structure, with Regulation (0.272) and Electric Grid (0.272) receiving the highest importance. In contrast, lower-ranked countries exhibit systemic deficiencies across multiple domains. This research offers a dual contribution: methodological innovation in readiness assessment and an actionable tool for policy instruments. The primary policy conclusion is clear: robust regulatory frameworks and strategic planning are the pivotal enabling conditions for ORE development, while industrial capacity and infrastructure are consequent steps that must follow, not precede, a solid policy foundation