Estimation of Ground NO2 Measurements from Sentinel-5P Tropospheric Data through Categorical Boosting

Atmospheric pollution has been largely considered by the scientific community as a primary threat to human health and ecosystems, above all for its impact on climate change. Therefore, its containment and reduction are gaining interest and commitment from institutions and researchers, although the solutions are not immediate. It becomes of primary importance to identify the distribution of air pollutants and evaluate their concentration levels in order to activate the right countermeasures. Among other tools, satellite-based measurements have been used for monitoring and obtaining information on air pollutants, and over the years their performance has increased in terms of both resolution and data reliability. This study aims to analyze the NO2 pollution in the Emilia Romagna Region (Northern Italy) during 2019, with the help of satellite retrievals from the {\nobreak Sentinel\nobreak-5P} mission of the European Copernicus Programme and ground-based measurements, obtained from the ARPA site (Regional Agency for the Protection of the Environment). The final goal is the estimation of ground NO2 measurements when only satellite data are available. For this task, we used a Machine Learning (ML) model, Categorical Boosting, which was demonstrated to work quite well and allowed us to achieve a Root-Mean-Square Error (RMSE) of 0.0242 over the 43 stations utilized to get the Ground Truth values. This procedure, applicable to other areas of Italy and the world and on longer timelines, represents the starting point to understand which other actions must be taken to improve its final performance

A Machine Learning Approach to Long-Term Drought Prediction using Normalized Difference Indices Computed on a Spatiotemporal Dataset

Climate change and increases in drought conditions affect the lives of many and are closely tied to global agricultural output and livestock production. This research presents a novel approach utilizing machine learning frameworks for drought prediction around water basins. Our method focuses on the next-frame prediction of the Normalized Difference Drought Index (NDDI) by leveraging the recently developed SEN2DWATER database. We propose and compare two prediction methods for estimating NDDI values over a specific land area. Our work makes possible proactive measures that can ensure adequate water access for drought-affected communities and sustainable agriculture practices by implementing a proof-of-concept of short and long-term drought prediction of changes in water resources.Comment: 4 pages, 3 figures, 1 table, IEEE IGARSS 2023 Conferenc

Electrophysiological findings in migraine may reflect abnormal synaptic plasticity mechanisms. A narrative review

Background: The cyclical brain disorder of sensory processing accompanying migraine phases lacks an explanatory unified theory. Methods: We searched Pubmed for non-invasive neurophysiological studies on migraine and related conditions using transcranial magnetic stimulation, electroencephalography, visual and somatosensory evoked potentials.We summarized the literature, reviewed methods, and proposed a unified theory for the pathophysiology of electrophysiological abnormalities underlying migraine recurrence. Results: All electrophysiological modalities have determined specific changes in brain dynamics across the different phases of the migraine cycle. Transcranial magnetic stimulation studies show unbalanced recruitment of inhibitory and excitatory circuits, more consistently in aura, which ultimately results in a substantially distorted response to neuromodulation protocols. Electroencephalography investigations highlight a steady pattern of reduced alpha and increased slow rhythms, largely located in posterior brain regions, which tends to normalize closer to the attacks. Finally, nonpainful evoked potentials suggest dysfunctions in habituation mechanisms of sensory cortices that revert during ictal phases. Conclusion: Electrophysiology shows dynamic and recurrent functional alterations within the brainstem-thalamuscortex loop varies continuously and recurrently in migraineurs. Given the central role of these structures in the selection, elaboration, and learning of sensory information, these functional alterations suggest chronic, probably genetically determined dysfunctions of the synaptic short- and long-term learning mechanisms

Un luogo sospeso nel tempo - progetto di restauro e valorizzazione per Villa Cesarini e il suo parco.

Situata nel comune di Corinaldo (AN), questa dimora signorile viene edificata su precedenti resti dal botanico Paolo Spadoni attorno alla metà del Settecento. Passata poi in mano a diversi proprietari, di cui l’ultimo risulta essere il Conte Giacomo Cesarini Romaldi, assume attorno agli inizi del novecento l’attuale aspetto. L’intero sistema si compone di diverse parti: la residenza del signore, la chiesa, la limonaia, la stalla, la rimessa delle carrozze e la casa del custode, connessi tra loro dal grande parco di circa due ettari di superficie, all’interno del quale sono disseminati innumerevoli manufatti, grotte e reperti archeologici. La presente tesi si pone come obiettivo quello di proporre un progetto di restauro che risponda alla richiesta del luogo di essere salvato; in esso sono stati riconosciuti dei valori che vanno necessariamente recuperati e trasmessi

Automatic dataset builder for Machine Learning applications to satellite imagery

In this paper, the architecture of an innovative tool, enabling researchers to create in an automatic way suitable datasets for Artificial Intelligence (AI) applications in the Earth Observation (EO) context, is presented. Two versions of the architecture have been implemented and made available on Git-Hub, with a specific Graphical User Interface (GUI) suitable for non-expert users. The tool has been designed to work with different types of sensors, but up to now has been tested with Sentinel-2 and Sentinel-1 data. We strongly believe that this tool will be of great usefulness for researchers applying AI to EO and Remote Sensing (RS). At the best of our knowledge, there is not a similar freely available tool, collecting the same benefits