Nicolaus Copernicus University

Introduction Resting‑state fMRI is widely used to map brain connectivity, yet the field lacks a clear, data‑driven standard for acquisition duration. We compared traditional Single‑Band (SB) imaging with an accelerated Multiband factor 4 (MB4) protocol to identify a time‑efficient approach for capturing stable network topology.Methods Twenty‑five healthy young adults completed two sessions one month apart, each including counterbalanced 13‑minute SB and MB4 scans. Test–retest reliability was evaluated across multiple durations using Portrait Divergence, a multiscale graph‑theoretic metric. Supplementary analyses compared Portrait Divergence with the field-standard Intraclass Correlation Coefficient, assessed robustness across preprocessing pipelines and parcellation schemes, and examined MB4’s ability to capture both cortical and subcortical connectivity.Results MB4 consistently outperformed SB across all tested durations, with peak topological stability at 9 minutes. Stability declined beyond this point, likely due to cumulative motion artefacts. Supplementary analyses confirmed MB4’s robustness across preprocessing strategies and atlas choices and demonstrated effective whole‑brain coverage.Conclusions In healthy young adults, MB4 captures network topology more effectively than SB and reaches peak stability at 9 minutes before declining, challenging the assumption that longer scans are always better. These findings require validation in clinical, paediatric, and geriatric cohorts.

Drought is an increasing concern in Morocco’s semi-arid regions, where water resources are under growing pressure from climate variability and land degradation. Most previous studies on drought monitoring in Morocco have examined meteorological or hydrological dimensions separately, overlooking vegetation dynamics. This fragmented approach limits the ability to capture the full spectrum of drought impacts across natural and managed ecosystems. This study assesses drought dynamics in the Ahmed El Hansali Dam basin, located in the Upper Oum Er-Rbia watershed, during 1975–2023. The novelty of this research lies in the long-term, combined assessment of meteorological, hydrological, and remote sensing indices at the basin scale, providing one of the most comprehensive drought evaluations for Morocco’s inland watersheds. A multi-index approach combining Standardized Precipitation Index (SPI), Streamflow Drought Index (SDI), and Normalized Difference Vegetation Index (NDVI) was applied to assess meteorological, hydrological, and vegetation responses to drought. Meteorological data from five stations, hydrological inflows at the dam, and Landsat-derived NDVI composites were utilized. The SPI and SDI revealed a marked increase in drought frequency and severity beginning in the early 1990s, with the most critical period between 2020 and 2023. SPI values below − 1.5 and SDI near − 1.6 confirmed intensifying precipitation deficits and declining inflows. The NDVI showed moderate interannual variability with lower values during drought years, though weak correlation with SPI and SDI suggests that vegetation response is influenced by altitude, land cover, and soil water retention, and agricultural practices including irrigation. Spatial analyses highlighted the greater resilience of forested highlands compared to lower-elevation agricultural areas. Overall, the combined use of these three indices offers a robust picture of drought evolution in the basin. By bridging the gap between single-dimension studies and proposing a comprehensive multi-source monitoring framework, this work provides a more holistic understanding of drought impacts and delivers actionable insights for water resource managers and policymakers.

Air quality improvement represents a critical challenge for the European Union, with particulate matter (PM) being the most harmful pollutant in urban areas. Urban Green Infrastructures (UGIs) provide essential ecosystem services that mitigate air pollution, notably through PM10 removal via deposition on leaf surfaces, reducing health risks associated with poor air quality. This study quantifies the PM10 removal supplied by urban forests in the Bydgoszcz-Toru & nacute; area (Poland) using a spatially explicit modeling framework. Remotely sensed Leaf Area Index, vegetation cover, and PM10 concentration data were integrated within a GIS environment, with all analyses conducted on a seasonal basis to capture temporal variability in vegetation phenology and pollutant levels. Resulting maps of mean seasonal PM10 removal efficiency (kg/ha) reveal distinct functional group patterns: deciduous broadleaves reach peak efficiency in summer, whereas conifers provide a more consistent year-round contribution, resulting in the highest annual removal. Monetary valuation was performed using externality costs from the European Environmental Agency. Overall, urban forests remove 3360.40 Mg of PM10 annually, corresponding to an estimated value of 255.69 M & euro;. Integrating biophysical and economic perspectives supports urban planning and highlights UGIs as nature-based solutions to enhance air quality, protect public health and promote ecosystem biodiversity and resilience.

Municipal sewage sludge is an environmental liability but also an energy-rich biomass that can support circular economy resource recovery. Here, we benchmark thin-layer drying of dewatered municipal sewage sludge (sludge cake) (40 g; layer thickness <= 5 mm) under open-air, convective hot air (40-150 degrees C), and microwave (70-1200 W) conditions to quantify drying kinetics, hygienisation indicators, and a screening-level energy-use proxy. High-power microwave drying reduced the time to constant mass from 32 h (open air) and 25 h 05 min (40 degrees C convection) to 20 min (900 W) and 14 min 05 s (1200 W). Faecal indicators (total/thermotolerant coliforms and presumptive Escherichia coli) were below detection after >= 100 degrees C convection or >= 300 W microwave treatment, while mesophilic aerobes and sulfite-reducing Clostridium spp. decreased by similar to 3-4 log10 with increasing exposure. A dragonfly-optimised epsilon-support vector regression model (DA-SVR) predicted drying trajectories across modes (overall RMSE approximate to 0.79 percentage points; held-out RMSE approximate to 1.47; R-2 >= 0.99). Overall, microwave thin-layer drying coupled with DA-SVR decision support enables constraint-based screening of sewage-sludge conditioning windows for logistics and thermal valorisation pathways; the framework can be extended to incorporate additional analytical endpoints where available.