Rare earth elements (REEs) are increasingly recognised as emerging contaminants, yet their transfer from sediment and water to biota remains poorly understood in coastal marine systems characterised by shallow-water hydrothermal vents. These vents release REEs at concentrations approaching those of anthropogenically impacted sites, providing a natural laboratory to investigate their transfer to marine biota. REE distribution and sediment-to-biota transfer were characterised along the pH gradient of the shallow CO2-vent system of Levante Bay (Vulcano Island, southern Tyrrhenian, Italy), combining sediment analyses with those of three co-occurring, functionally distinct organisms representing complementary exposure pathways: the seagrass Cymodocea nodosa, the sea anemone Anemonia viridis, and the benthic goby Gobius incognitus. REE concentrations decreased overall along the sequence: sediment > C. nodosa > G. incognitus > A. viridis. Biota-Sediment Accumulation Factor (BSAF) values were below unity in all matrices at both areas, indicating limited transfer despite hydrothermal enrichment. Post-Archean Australian Shale (PAAS) normalised REE patterns, diagnostic anomalies and multivariate ordination revealed three distinct biotic signatures in species sharing the same hydrothermally enriched environment: a sediment-like, light REE-enriched profile in A. viridis; a relatively heavy REE-enriched, biologically fractionated profile in C. nodosa; and a low, dispersive profile in G. incognitus. The latter was the only matrix showing higher BSAF at both low-pH sites, possibly reflecting the dietary pathway. Differences in REE accumulation among matrices exceeded those between the two pH conditions. These results indicate that co-occurring species exhibit distinct REE signatures and that the choice of bioindicator embeds a specific exposure pathway.
The 2015–2016 El Niño was one of the most intense events in the Mexican Tropical Pacific and the Gulf of California, characterized by increased temperatures and shifts in water-mass proportions. To quantify these variations and their impact on carbon biogeochemistry, an Optimum Multiparameter (OMP) analysis was implemented using temperature, salinity, and dissolved oxygen (O2) data from three oceanographic cruises (June 2015, March 2016, and October 2017) and Argo float profiles. Results showed that Tropical Surface Water (TSW) contributed ∼50% or more of the upper ∼50 m in June 2015 and > 50% of the upper ∼100 m in March 2016, deepening both the oxycline and the dissolved inorganic carbon gradient (hereafter, the carboncline, defined as the depth of the maximum DIC vertical gradient). This TSW intrusion reconfigured the vertical structure of the water column, directly affecting the biogeochemistry of O2 and dissolved inorganic carbon (DIC). Estimates of biogeochemical residuals (ΔDIC and ΔO2) revealed distinct processes: in June 2015, residuals in the subsurface layer (70–140 m), where Gulf of California Water (GCW) and Subtropical Subsurface Water (StSsW) mix, were qualitatively consistent with aerobic remineralization, although the ΔDIC–ΔO2 regression was not statistically significant. In contrast, in March 2016, a statistically significant photosynthetic signal (ΔDIC < 0, ΔO2 > 0) was detected in the surface layer (0–50 m), spatially coincident with a cyclonic mesoscale eddy observed during the cruise, which may have locally enhanced nutrient supply to the euphotic zone. Under El Niño-Southern Oscillation (ENSO) neutral conditions in October 2017, no statistically detectable ΔDIC–ΔO2 relationship was found. These findings suggest that biogeochemical residuals can distinguish contrasting biogeochemical conditions, ranging from subsurface residual patterns consistent with remineralization to a surface photosynthetic signal coincident with mesoscale activity, within the oxygen minimum zone (OMZ) of the Mexican Tropical Pacific, even though the two El Niño snapshots examined here did not yield a single, generalizable biogeochemical signature of ENSO forcing.
This work presents the experimental characterization and empirical modeling of the dielectric properties at microwave frequencies of two conductive liquid systems: de-ionized water/herbicide mixtures and lithium-salt electrolyte solutions, relevant to environmental/health monitoring and energy storage applications, respectively. Measurements are performed using the coaxial probe technique over a frequency range starting from 500 MHz and up to some tenths of GHz. The dielectric permittivity at various concentration levels is modeled using the single-pole Debye model with frequency-dependent conductivity component. The electrode polarization effect at the probe-sample interface is systematically analyzed, proposing a low-frequency measurement limit that shifts toward higher frequencies as the conductivity increases; a relative-deviation analysis on both permittivity components quantitatively assesses this limitation. Within the validated frequency range, the Debye formulation accurately reproduces the properties of both liquid systems, showing that conductive and interfacial effects dominate at the lower frequencies while dipolar relaxation prevails at higher frequencies.
Freelance workers must continually acquire new skills to remain competitive in online labor markets, yet they lack the organizational training, mentorship, and infrastructure available to traditional employees. Generative AI-powered tools like ChatGPT are reshaping market skill demands while also offering new forms of on-demand learning support to meet those demands. Despite growing interest in AI-powered learning tools, little is known about how freelancers actually use these tools to learn, the challenges they encounter, and how generative AI for learning interacts with precarity and competition in platform-based work. We present a mixed-methods study combining a survey and semi-structured interviews with freelance knowledge workers. Grounded in self-directed learning theory, we examine how freelancers integrate generative AI tools into their learning practices. Our findings show that freelancers increasingly rely on generative AI to structure learning and support exploratory skill acquisition, but do not treat it as their primary learning resource due to inconsistency, lack of contextual relevance, and verification overhead. We identify a shift from learning as growth to learning as survival, where upskilling is oriented toward immediate market viability rather than long-term development. We also surface a structural challenge we term invisible competencies, in which workers acquire skills through generative AI tools but lack credible ways to signal or validate these skills in competitive freelance markets. Based on these insights, we offer design recommendations for generative AI-powered learning tools for freelancers.
As debates over the impact of smartphones on young people intensify, we urgently need ways to identify which smartphone behaviours are related to poorer mental health. While many approaches focus on specific harmful or beneficial smartphone activities, here we instead suggest that the relationship between smartphones and mental health may partly depend on how users transition between activities. To test this, we develop a novel network analysis method to analyze objective smartphone data. We construct networks capturing how each user journeys across mobile Apps within a smartphone usage session, which we term ‘App-journey’ networks. Across a dataset of participants aged 18-22 (n = 82), exploratory statistical analyses show that features of ‘App-journey’ networks correlate with user mental health and attentional control. For example, users with higher depressive symptoms typically transition to a wider variety of Apps within each phone session (higher network density) and are less likely to confine each phone session to repeated groups of Apps (lower network modularity). Additionally, specific Apps which users report using more automatically are more central in App-journey networks than Apps users report using less automatically. In contrast to features of App-journey networks, time spent on one’s phone is not significantly related to mental health. In summary, this novel method to study smartphone use abstracts away from activities themselves and instead quantifies patterns of activity transitions. We show such transition patterns are related to user mental health and subjective experiences of automatic use, thus representing targets for future research and interventions.
