Parthenope University of Naples
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Detection of Exoplanets in Transit Light Curves with Conditional Flow Matching and XGBoost
No full textCoastal Responses to Holocene Sea Level Rise in the Fondi Plain (Central Italy) by means of new borehole and geomorphologic data.
No full textFiscal incentives for energy poverty in Italy: Bridging the gap or missing the mark?
No full textThis study evaluates the effectiveness of Italian fiscal incentives for energy retrofitting, with a particular focus on their role in addressing energy poverty. It examines the distribution of these incentives across households, assessing their impact on energy-vulnerable groups using well-established energy-poverty indicators. Drawing on data from the 2022 Household Budget Survey by the Italian National Institute of Statistics (ISTAT), the analysis employs Propensity Score Matching (PSM) to determine the extent to which tax credits for energy-efficient renovations benefit energy-poor households—an aspect of policy effectiveness largely overlooked in the literature.
The findings reveal that higher-income households disproportionately benefit from these incentives, highlighting inefficiencies in targeting mechanisms. Despite promoting energy efficiency improvements, fiscal subsidies remain largely inaccessible to low-income, energy-poor households. The study underscores the need for policy refinements, such as income-based eligibility criteria and enhanced outreach efforts, to ensure more equitable access to energy-saving incentives. Furthermore, it acknowledges data limitations, particularly the absence of longitudinal tracking, and calls for more granular data collection to assess long-term impacts effectively. These insights contribute to the broader discourse on optimizing fiscal policies to mitigate energy poverty and support sustainable energy transitions
Fiber optic lossy mode resonance sensors for chemical sensing
No full textThis work presents the fabrication and characterization of a fiber optic gas sensor utilizing Lossy Mode Resonances (LMR) achieved with a nanoscale polyphenylene oxide (PPO) coating. The PPO coating is applied to the cladding removed region of a multi-mode silica fiber, simultaneously serving as both the LMR support layer and the sensing material. The PPOcoated LMR sensor is exposed to various concentrations of ammonia as a case study. The sensor demonstrates a high sensitivity of 0.25 nm/ppm within the ammonia concentration range of 2.5 to 37.5 ppm. These results highlight the potential of this PPO-based fiber optic sensor for efficient and sensitive detection of hazardous compounds in different settings, ranging from medical to industrial
Transport Mechanisms and Pollutant Dynamics Influencing PM10 Levels in a Densely Urbanized and Industrialized Region near Naples, South Italy: A Residence Time Analysis
No full textThis study explores the transport mechanisms and pollutant dynamics influencing particulate matter concentrations at the Pomigliano d’Arco monitoring site, situated in a densely urbanized and industrialized region near Naples, Southern Italy, where daily PM10 averages consistently exceed EU thresholds. Exploiting an innovative residence time analysis, based on backward-trajectory analysis with the HYSPLIT model, we investigated air mass histories from 2018 to 2023 to identify predominant pollutant transport pathways and their temporal dynamics. Seven distinct airflow clusters were identified, with the most frequent originating from the western and northeastern directions, influenced by local circulation and long-range transport from the central Mediterranean and northern Africa. Seasonal variations revealed elevated PM10 levels during winter months, attributed to increased residential heating and temperature inversions, as well as summer peaks linked to Saharan dust transport and secondary aerosol formation. The residence time analysis highlighted regions within the central Mediterranean and northern Africa as significant contributors to high PM10 concentrations at the monitoring site, emphasizing the role of both local emissions and transboundary pollution. These findings provide critical insights for policymakers and air quality managers to develop targeted mitigation strategies aimed at reducing PM pollution in urban and industrialized areas, thereby enhancing public health and environmental sustainability
How Do Local Economic Structures Influence the Variability of Land Sensitivity to Degradation in Italy?
No full textThis study examines the relationship between local economic structures and environmental sensitivity in Italy, focusing on a novel indicator that estimates the spatial variability of the Environmentally Sensitive Area Index (ESAI) over time. This approach captures within-region disparities in degradation processes, addressing a key gap in the existing literature. Using a dataset covering all Italian provinces from 1960 to 2010 and considering multiple socio-economic variables, the research evaluates their impacts on ESAI variability. In particular, this study adopts a spatial autoregressive model (SAR), which allows both direct and indirect effects of selected predictors to be captured. The
findings offer insights for policymakers in designing strategies to mitigate the spread of land degradation hotspots and promote strategies that balance environmental conservation with socio-economic development to ensure resource sustainability
Highly sensitive gold nanostar based optical fiber sensor with tunable plasmonic resonance
No full textIn this work, we present a detailed numerical and experimental investigation of highly sensitive optical fiber sensors based on localized surface plasmon resonance (LSPR). These sensors are enhanced by the deposition of nanoparticles (NPs) and nanostars (NSs) onto uncladded silica multi-mode optical fiber. The unique optical properties of NSs - featuring a 40 nm gold core surrounded by silver branches of variable size and shape - allows for precise tuning of the LSPR effect. For comparison, we also explored spherical gold NPs with a 40 nm diameter to assess performance differences. Our findings, both numerical and experimental, demonstrate that the LSPR wavelength and sensitivity to surrounding refractive index can be finely tuned by adjusting the morphology of the NS branches. This is achieved by varying the silver nitrate content during their synthesis. Using the Finite Element Method-based design tool we performed simplified study cases, that led to experimental sensitivity of approximately 560 nm/RIU for an LSPR wavelength near 810 nm. As a practical demonstration, the sensor was successfully employed to detect Thiram, a common agricultural pesticide, with a wide dynamic range from 10 pM to 100 μM and an impressive low limit of detection of 0.3 pM. Moreover, we investigated the sensor selectivity, stability and response to environmental temperature changes. This study emphasizes the simplicity, cost-effectiveness, and tunable performance of NS-based optical fiber sensors. By manipulating nanostructure morphology, we can significantly enhance sensor performance, positioning this technology as a highly promising solution for environmental monitoring, biomedical diagnostics, and chemical detection
Spatio-temporal prediction using graph neural networks: A survey
No full textThe analysis of spatial time series is increasingly relevant as spatio-temporal data are becoming widespread due to the ever-growing diffusion of data acquisition devices. Spatio-temporal prediction is crucial for grasping insights on spatio-temporal dynamics in diverse domains. In many cases, spatio-temporal data can be effectively represented using graphs, thus making Graph Neural Networks the most sounding deep learning architecture for the modelling of spatio-temporal series. The aim of the work is to provide a self-consistent and thorough overview on Graph Neural Networks for spatio-temporal prediction, giving a taxonomy of the diverse approaches proposed in the literature. Moreover, attention is paid to the description of the most used benchmarks and metrics in different real-world spatio-temporal domains and to the discussion of the main drawbacks of spatio-temporal Graph Neural Networks. Furthermore, unlike other similar works on deep learning, statistical methods for spatio-temporal modelling are briefly surveyed in this work. Finally, insights on future developments of Graph Neural Networks for spatio-temporal prediction are suggested