Tuscia University

We investigate the structure and dynamics of paid visibility on social media platforms by analyzing the advertising behavior of political parties and news outlets across six European countries (France, Germany, Italy, Poland, Spain, and the United Kingdom) from 2019 to 2024. Drawing on ∼291k advertising campaigns and ∼7M organic posts from Meta’s Ad and Content Libraries, we employ descriptive analytics, linear and nonlinear modeling, and panel regression techniques to investigate advertising activity and quantify the relationship between spending patterns and exposure metrics. Beyond enhancing the transparency of the advertising process, our analysis reveals three key findings: (1) In four out of six countries, political parties are the primary users of social media advertising, with a small number of pages accounting for the majority of spending and exposure. (2) Posts exposure grows rapidly with low levels of advertising expenditure but saturates beyond moderate thresholds, suggesting the presence of bounded attention markets. (3) Advertising spending is positively associated with overall page performance. Furthermore, when addressing the challenging task of comparing paid and organic visibility, we observe a positive increase in the spending coefficient, particularly for political parties, which consistently generate more impressions than news pages. These results offer empirical evidence that visibility operates as a platform-mediated market, shaped by algorithmic constraints, economic incentives, and strategic competition. By empirically mapping the structure and distribution of paid attention, this study offers a data-driven contribution to enhance platform transparency and accountability.

Sustainable forest operations require operational planning tools that effectively integrate productivity, environmental conservation, and social acceptability, particularly within complex and environmentally sensitive forest systems. In Mediterranean small-scale forestry, harvesting decisions are frequently guided by expert judgment rather than by systematic and transparent planning frameworks. This reliance on subjective decision making can result in heterogeneous management practices and, in some cases, suboptimal operational outcomes. This study aims to validate a GIS-based Analytic Hierarchy Process (GIS-AHP) decision support system for the selection of harvesting and wood systems in the chestnut coppices of central Italy and to assess the robustness of its recommendations when expert judgments are provided by different stakeholder groups. The methodology integrates spatial data and multi-criteria analysis to evaluate the suitability of three extraction systems (forwarder, cable skidder, and cable yarder) and three wood systems (Cut-To-Length, Whole-Tree Harvesting, and Tree-Length) across 162 Forest Management Units (1332.5 ha), using weights elicited from four stakeholder categories (researchers, technicians, forest owners, and workers; n = 144). Results show statistically significant differences in mean suitability values among stakeholder groups for all systems; however, convergence at the operational decision level is high. The cable skidder is recommended over 94%-100% of the area depending on the stakeholder category, with full agreement among all groups in 87.7% of the Forest Management Units. For wood systems, Whole-Tree Harvesting is selected over 96.1% of the analysed area, with agreement in 95.1% of the Forest Management Units. Divergences are therefore limited and attributable to differences in AHP weighting structures. Overall, the findings demonstrate that the GIS-AHP approach provides stable and transferable recommendations despite variability in expert perspectives, supporting its applicability as a transparent and robust decision support tool for operational planning in chestnut coppices and similar Mediterranean forest systems.

A novel methodology is proposed for jointly modeling the price dynamics of natural gas and electricity by integrating graph-based Machine Learning and optimal transport theory. The framework combines visibility graph embeddings with the Wasserstein barycenter to uncover latent structures and asymmetric dependencies between the two interconnected energy markets. Log-return time series are first transformed into visibility graphs and then embedded into high-dimensional vector spaces, where complex temporal and structural patterns become more discernible. In the embedding space, an information-driven Wasserstein barycenter is computed by optimizing the barycenter weights via Shannon entropy maximization. This procedure reveals an asymmetric balance between the two markets, with natural gas exerting a structurally dominant influence. To characterize the joint stochastic dynamics, a Gaussian Mixture Model is fitted to the thus determined unbalanced Wasserstein barycenter using maximum likelihood estimation via the Expectation-Maximization algorithm. An additional Gaussian component is introduced for each commodity to capture market-specific behavior. The resulting model can be calibrated to match the first four moments of the empirical log-return distributions and their observed correlation. Applied to Italian market data from 2019 to 2023, a period marked by extreme volatility and systemic shocks, the methodology accurately reproduces both common dynamics and idiosyncratic deviations. The analysis reveals that the entropy-optimal barycentric weights are lambda(ng)=0.65 for natural gas and lambda(el)=0.35 for electricity, highlighting a dominant role of the natural gas market in the joint representation. Compared with a comprehensive benchmark of GARCH-type models, the proposed framework exhibits markedly superior empirical performance. The approach provides a robust, interpretable, and adaptable tool for risk analysis, derivative pricing, and the study of structural interactions across energy markets.

The Turtle Dove is a regular migratory species widely distributed in Italy, though the information on its abundance in each Italian region is modest; thus, action plans have been implemented to improve its conservation. This is a preliminary study meant to provide information on the distribution and abundance of the TD in Apulia. We analyzed data collected during 2019-2023 within the Farmland Bird Index (FBI) project, whose sampling design was intensified to achieve more homogeneous coverage of the region. The survey method was based on unlimited-distance point counts lasting 10 min. Counts were carried out between 15 May and 15 June of every year, beginning from dawn until 12:00 AM, with each station visited once. A total of 211 TD birds were recorded across 147-point counts. The MaxEnt analysis showed that olive orchards, needle-leaved woodlands, and evergreen broad-leaved woodlands had a positive effect on species occurrence, whereas winter precipitation had a negative effect. The total estimate of pairs fell within the range 47.14-66. The estimated density for TD in Apulia was 0.87-1.16 birds/km2, while that of pairs was 0.69-0.97/km2. By relating the estimated densities to the area suitable for the species' presence, the abundance of TD was estimated at approximately 17,337-24,303 birds.

This study evaluates the efficacy and impact of fiber-laser cleaning as a more sustainable, non-chemical alternative to traditional chemical stripping for removing polyurethane paint and epoxy primer from Al2024-T3 Alclad aluminum sheets widely used in the aeronautic sector. Current chemical methods pose significant risks due to volatile organic compounds and hazardous waste, motivating the search for safer, automated, and environmentally friendly processes. The investigation compared the effect of the chemical stripping and optimized laser parameters on the Al2024-T3 alclad alloy through a comprehensive analysis. A full Design of Experiment (DoE) was used to vary laser cleaning parameters, followed by evaluation of surface morphology (optical, SEM, 3D profilometry), and mechanical properties (hardness, yield strength). The main findings indicate that within a qualified process window (Pa 100