Parthenope University of Naples
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Dual-Polarimetric Sentinel-1 SAR Backscattering Features From Green Macroalgae Floating in the Coastal Ocean
No full textGreen macroalgae (GMAs) are prominently visible as distinctive bright mats on synthetic aperture radar (SAR) imagery, which are frequently used to complement conventional optical observations for extracting GMA distribution at sea. However, the mechanisms ruling the interaction between the microwaves and the GMA are not well understood. This study aims to address this gap by systematically analyzing C-band backscattering from GMA-covered sea surfaces from a physical perspective using dual-polarized Sentinel-1 SAR imagery combined with a three-layer description of the GMA-covered sea surface. First, the dual-polarized normalized radar cross section (NRCS) is analyzed showing that the co-polarized backscatter is always above the system noise, while this is not the case for the cross-polarized backscatter that is frequently noisy. Then, polarimetric descriptors are adopted to shed light on the GMA backscatter mechanisms. The degree of polarization (DoP) is evaluated to demonstrate that the electromagnetic (EM) wave backscattered from the GMA is almost fully polarized, indicating a negligible depolarized component. Finally, the polarization ellipse associated with the EM wave backscattered from the GMA is analyzed, showing that the GMAs call for a linearly polarized (almost vertically oriented) backscattered EM wave. This finding suggests a scattering mechanism dominated by single-reflection and residual multiple-reflection components within the wet-GMA layer of the three-layer schematic model. The analysis is extended to several imageries collected under different green tide stages, showing that the abovementioned findings always apply
Investimenti temporanei in società di capitali e partecipazioni "autoestinguibili"
No full textLo scritto esplora le caratteristiche e le condizioni di ammissibilità di partecipazioni sociali “autoestinguibili” nel diritto comune delle società di capitali. In coerenza con le origini della figura nella disciplina speciale delle società miste, l’indagine attribuisce rilievo centrale al collegamento funzionale della temporaneità della partecipazione sociale con la sottostante operazione finanziaria che la giustifica. In questa chiave si esclude la compatibilità di un’estinzione senza liquidazione di partecipazioni a termine con la causa lucrativa del contratto di società (ancor prima che per contrasto con il divieto del patto leonino), reputandosi invece ammissibile una condizione risolutiva di “auto-estinzione” senza liquidazione al conseguimento del rimborso del valore attuale della partecipazione sociale temporanea. L’indagine procede all’individuazione di criteri e procedimento di liquidazione ed estinzione della partecipazione temporanea, passando infine ad individuare i possibili accorgimenti statutari per la prevenzione della riduzione del capitale e dello scioglimento della società
G-Litter Marine Litter Dataset Augmentation with Diffusion Models and Large Language Models on GPU Acceleration
No full textMarine litter detection is crucial for environmental monitoring, yet the imbalance in existing datasets limits model performance in identifying various types of waste accurately. This paper presents an efficient data augmentation pipeline that combines generative diffusion models (e.g., Stable Diffusion) and Large Language Models (LLMs) to expand the G-Litter dataset, a marine litter dataset designed for autonomous detection in heterogeneous environments. Leveraging scalable diffusion models for image generation and Alpaca LLMs for diverse prompt generation, our approach augments underrepresented classes by generating over 200 additional images per class, significantly improving the dataset's balance. Training G-Litter augmented dataset using YOLOv8 for object detection demonstrated an increase in detection performance, improving recall by 7.82% and mAP50 by 3.87% (compared with baseline results). This study emphasizes the potential for combining generative AI with HPC resources to automate data augmentation on large-scale, unstructured datasets, particularly in edge computing contexts for real-time marine monitoring. The models were tested on real videos captured during simulated missions, demonstrating a superior ability to detect submerged objects in dynamic scenarios. These results highlight the potential of generative AI techniques to improve dataset quality and detection model performance, laying the foundation for further expansion in real-time marine monitoring
Exploring metaverse innovations in public administration: A focus on human resource management
No full textDalla devianza alla speranza. Storie di riscatto e di impegno civile
No full textLe nuove forme di fragilità sociale causa di diseguaglianze e povertà educative,
che in particolare investono i minori, rappresentano una questione
tornata prepotentemente al vertice delle emergenze sociali (Censis,
2024). I minori di cui parliamo sono quelli provenienti da contesti sociali
fragili e culturalmente deprivati spesso caratterizzati da percorsi formativi
incerti, da frequenza scolastica intermittente, abitualmente attraversati
da un comune destino di incuria educativa e di abbandono morale.
Ragazzi nati in famiglie caratterizzate da una vera e propria “eclissi di responsabilità
genitoriale”, attraversati da una frattura verticale della relazione
primaria perché privati troppo presto delle figure di attaccamento
e di cura (Iavarone, Scuotto, 2024). Spesso, in queste famiglie, l’adulto si
rapporta in maniera malsanamente simmetrica con il minore, con il quale
intrattiene una relazione di complicità, quasi amicale, diventando una
specie di sodale che compete per immaturità e incompiutezz
A sensitivity study of vulnerability parameters for rocking masonry façades of single-nave churches hit by the 2016 Central Italy seismic sequence
No full textThe paper is devoted to the large-scale assessment of the vulnerability of masonry church façades to the simple out-of-plane rocking mechanism. The main goal is to derive fragility curves assessing the influence of some geometrical and mechanical parameters and the effect provided by tie rods on the façade vulnerability to this type of mechanisms. According to the displacement-based approach, pushover analysis for each façade is carried out considering both the stabilizing contribution of the interlocking with the sidewalls and the presence of tie rods. The analyses are addressed with reference to a large sample of masonry churches generated starting from the geometrical parameters of the churches hit by the 2016-17 Central Italy seismic sequence using the damage data extracted from the DaDO WebGIS. Geometrical variables, i.e. the length, the height and the thickness of the façade, are treated by means of Monte Carlo simulation to generate a numerical sample of 800 façades. Three limit states are considered, corresponding to the onset of rocking and to moderate and severe motions, and fragility curves are derived for these façades subjected to several ground motions scaled to different values of PGA, using incremental static analysis and multiple stripe analysis. The sensitivity of the fragility curves is investigated with reference to the variation of: i) the friction coefficient influencing the contribution exerted by the sidewalls, ii) the length of masonry units, and iii) the position of the façade mass centre. Moreover, the contribution of strengthening systems made with steel tie rods to mitigate the simple rocking is analysed in terms of vulnerability reduction related to the diameter, position, pre-tension and ultimate strain of the tie rods. Finally, the effect of different seismic inputs on the seismic vulnerability of façades under moderate and severe motions is also examined
Caenorhabditis elegans in the research of plants-derived nutraceuticals: a VOSviewer-based trend analysis
No full text
Artificial Intelligence application in platforms: opportunities and risks
No full textThe application of AI in digital platforms represents a revolution that makes numerous
opportunities possible but also poses significant risks. Indeed, through automated processes it helps to improve the user experience and facilitate activities such as e-commerce and smart contracts. However, these advances are accompanied by risks related to the rigidity of AI systems which can lead to errors or harm users due to the absence of direct human intentionality, as in the case of smart contracts; and also related to challenges in protecting privacy. OPs, leveraging AI, process vast amounts of data in ways that may not fully comply with stringent regulations like the GDPR. In light of recent European regulations, including the DSA, the DMA and the AI Act, this Chapter critically examines these issues, addressing questions of liability, security and data protection to ensure a balanced approach to innovation and user rights
