Sci

Arsenic (As) accumulation in rice (Oryza sativa L.) is considered a major environmental and food safety concern, particularly in flooded agroecosystems where reducing conditions mobilize As from soils. Portugal is one of Europe’s rice producers, especially in the Tejo, Almansor, and Sorraia valleys. As such, this study evaluates As pathways across 5000 ha of rice fields in the Tagus, Sorraia, and Almansor alluvial plains by combining soil, water, and plant analyses with a geostatistical approach. The soils exhibited consistently elevated As concentrations (mean of 18.9 mg/kg), exceeding national reference values for agricultural soils (11 mg/kg) and forming a marked east–west gradient with the highest levels in the Tagus alluvium. Geochemical analysis showed that As is strongly correlated with Fe (r = 0.686), indicating an influence of Fe-oxyhydroxides under oxidizing conditions. The irrigation waters showed low As (mean of 2.84 μg/L for surface water and 3.51 μg/L for groundwater) and predominantly low sodicity facies, suggesting that irrigation water is not the main contamination vector. In rice plants, As accumulation follows the characteristic organ hierarchy roots > stems/leaves > grains, with root concentrations reaching up to 518 mg/kg and accumulating progressively in the maturity phase. Arsenic content in harvested rice grains was 266 μg/kg (with a maximum of 413.9 μg/kg), being close to EU maximum limits when considering typical inorganic As proportions, assuming 60 to 90% inorganic fraction. Together, the findings highlight that a combined approach is essential, and identify soil geochemistry (and not irrigation water) as the primary source of As transfer in those agroecosystems, due to the flooded conditions that trigger the reductive dissolution of Fe oxides, releasing As. Additionally, the results also identified the need for targeted monitoring in areas of elevated As content in soils and support future mitigation through As speciation analysis, cultivar selection, improved fertilization strategies, and water-management practices such as Alternate Wetting and Drying (AWD), to ensure the long-term food safety.

This study analyzes the observed patterns of Generative Artificial Intelligence (Generative AI) use by students in higher education through the lens of the sociotechnical systems (STS) theory, focusing on the academic subsystem. The empirical basis is a survey of 2083 students (3686 responses) from seven countries in Central and Eastern Europe, Central Asia, and Central Africa. Based on these data, two proxy indicators are proposed: A₁, reflecting the overall academic use of Generative AI and A₂, characterizing the use of Generative AI in a research context. The results show that Generative AI is widely incorporated into students’ academic activities (A₁ = 79.06%), while research-oriented use remains less common (A₂ = 46.66%) and varies significantly across subsamples. A joint analysis of A₁ and A₂, visualized as a zoned space A₁–A₂, reveals different configurations of academic practices: from a predominance of routine educational use to a more pronounced focus on research tasks. Cross-country comparisons show that in certain contexts (e.g., Kazakhstan and one of the Ukrainian subsamples), Generative AI is more often used in a research context, while in other cases, its use remains predominantly educational and routine. In this sense, the results indicate that Generative AI is beginning to fulfill the role of an emerging student research assistant in students’ academic life: technology has already become a familiar tool for completing educational tasks, but its use in supporting research activities remains fragmented. The proposed model and proxy indicators allow us to describe and compare current configurations of Generative AI use in the academic subsystem. The obtained results provide a basis for further research aimed at a deeper understanding of the factors determining the inclusion of Generative AI in student research practice, as well as for the development of management approaches regarding its use in higher education.

This study aimed to examine the association between physical activity and individuals’ health status, healthcare utilization, socio-demographic characteristics, and health behaviors in a large representative sample from Northern Greece. A cross-sectional study was conducted involving 1227 participants (47.4% males, mean age 49.94 ± 14.87 years) from Thrace, Greece, selected through a two-stage stratified sampling method. According to the Greek version of IPAQ, participants were classified as inactive/insufficiently active, sufficiently and highly active. Data on socio-demographic, lifestyle, and health-related variables were collected through structured interviews. Multivariate logistic regression analysis was performed to determine the independent effect of physical activity on subjects’ characteristics using SPSS ver. 19. Half of the participants (49.8%) were inactive/insufficiently active, 418 participants (34.1%) were sufficiently active, and 198 participants (16.1%) were highly active. In univariate analysis, smoking (p < 0.001), higher coffee consumption (p = 0.002), higher adherence to Mediterranean diet (p < 0.001), napping during the day (p = 0.017) and short sleep duration (p < 0.001) were associated with lower prevalence of high activity. In adjusted analyses, sufficiently active participants had a lower risk for bad self-rated health (aOR = 0.63), hypertension (aOR = 0.41), dyslipidemia (aOR = 0.42), diabetes (aOR = 0.53), obesity (aOR = 0.61), cardiovascular diseases (aOR = 0.43), anxiety (aOR = 0.65), depression (aOR = 0.56), daily sleepiness (aOR = 0.62), poor sleep quality (aOR = 0.71), as well as for primary (aOR = 0.54) and secondary (aOR = 0.40) healthcare utilization compared to inactive participants. Higher-intensity physical activity did not enhance these beneficial effects of sufficient activity on subjects’ characteristics. Physical inactivity significantly compromises health across multiple domains. Promoting even moderate-intensity physical activity may reduce chronic disease burden and healthcare utilization.