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.
The valorization of urban biowaste is central to circular bioeconomy strategies; however, acidogenic fermentation and chain elongation (CE) of real waste streams require exogenous electron donors and chemical buffering, limiting long-term sustainability. Achieving stable production of medium-chain fatty acids (MCFAs), particularly caproic acid (C6), under chemicals-free conditions remains challenging. This study investigates long-term fermentation and CE in a single mesophilic bioreactor treating urban substrates, without external electron donors or buffering agents. A thermally pretreated mixture of food waste and waste activated sludge was processed in a 5.5 L semicontinuous reactor operated under pulsed feeding, enabling endogenous, lactate-driven CE. Two feeding frequencies were tested at a constant organic loading rate of 10 gVS L-1 per feed: twice and three times weekly. In both cases, the system exhibited stable performance, with pH self-regulation in a mildly acidic range (approximate to 5.0-5.4) driven by coupled lactate production and consumption. Total fatty acid concentrations remained consistent (11.9-12.8 gCOD L-1), with even-chain species dominating the product spectrum (acetic similar to 33%, caproic similar to 30%, butyric similar to 21%). Lower feeding frequency maximized caproic acid productivity (31.0 +/- 1.5 mmolC L-1 d(-1)), whereas higher feeding frequency increased annualized caproate production by 29.5%, indicating a trade-off between per-cycle yield and cumulative throughput. Microbial analyses revealed a selective enrichment of lactate-producing Olsenella alongside CE-associated Caproiciproducens and Pseudoramibacter taxa. Biomethane potential tests of the fatty-acid-rich broth yielded 0.46-0.49 m(3)CH(4) kg(-1)VS, demonstrating effective downstream anaerobic digestion. Overall, these results demonstrate a robust, additive-free CE-AD platform for integrated waste-to-chemicals and energy recovery.
The Nectriaceae includes major plant and human pathogens, yet the genomic foundation underpinning its taxonomy remains uneven and largely unassessed. We analysed 1,530 genome sequence assemblies to quantify metadata completeness, geographic and taxonomic bias, and assembly quality across the family. One-third of the assemblies lacked essential metadata, sequencing was heavily skewed toward a few agriculturally important lineages, and sampling of many genera was limited or nonexistent. BUSCO and QUAST metrics revealed striking heterogeneity in assembly quality, with widespread fragmentation and a substantial subset of genomes falling outside the expected quality thresholds. From orthologous protein sequences of 763 single-copy genes in 576 high-quality genomes, we reconstructed a phylogenomic backbone for the Nectriaceae and quantified gene- and site-level concordance. While major clades broadly match current concepts, extensive gene-tree discordance and a polyphyletic Nisikadoi complex highlight unresolved evolutionary and taxonomic boundaries. Our study delivers the first integrated, family-wide evaluation of Nectriaceae genomic resources and outlines a framework for quality standards, curated metadata, and stable phylogenomic inference to support future taxonomic and comparative work. ### Competing Interest Statement The authors have declared no competing interest.
Understanding safety in complex socio-technical systems requires analytical approaches that move beyond linear accident models to examine how interactions across organisational, technical and operational elements shape safety outcomes. This study applies two systemic safety analysis approaches, Causal Analysis based on Systems Theory (CAST) and the Functional Resonance Analysis Method (FRAM), using well-documented aviation investigation data. The study examines how different systemic methods model system behaviour, frame causality and performance variability, and generate different forms of safety recommendations. CAST identified cross-level control and feedback weaknesses that enabled the wrong-surface alignment under the runway-closure night configuration. FRAM showed how coupled performance variability, including missed runway-closure cueing and ambiguous visual cues, shaped the development of misalignment risk while also clarifying the recovery pathway that enabled the go-around. This study suggests that CAST and FRAM are best used as complementary lenses for systemic event analysis. CAST supports governance and assurance redesign, while FRAM informs operational guardrails and variability management under uncertainty.
The complex relationship between climate shocks, adaptation, and migration hinders a rigorous understanding of the heterogeneous mobility outcomes of farm households exposed to climate stress. To unpack this heterogeneity, we couple causal machine learning methods, tailored to a conceptual framework bridging economic migration theory and the poverty traps literature, with longitudinal multi-topic household data from Nigeria. This approach allows us to produce fine-grained, non-parametric evidence that identifies the factors driving divergent mobility outcomes across different household groups affected by the same shocks. The estimated conditional average treatment effects suggest that key variables—pre-shock asset levels, in situ adaptive capacity, and weather exposure during the previous survey period—shape not only the magnitude but also the sign of the impact of agriculture-relevant weather anomalies on the mobility outcomes of farming households. While in situ adaptation serves as an alternative to migration, the role played by wealth constraints and multiple shock exposure is consistent with the existence of climate-induced immobility traps.
