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Journal articleDe Nardi A, Marini G, Dorigatti I, et al., 2025,
Quantifying West Nile virus circulation in the avian host population in Northern Italy
, Infectious Disease Modelling, Vol: 10, Pages: 375-386, ISSN: 2468-2152West Nile virus (WNV) is one of the most threatening mosquito-borne pathogens in Italy where hundreds of human cases were recorded during the last decade. Here, we estimated the WNV incidence in the avian population in the Emilia-Romagna region through a modelling framework which enabled us to eventually assess the fraction of birds that present anti-WNV antibodies at the end of each epidemiological season. We fitted an SIR model to ornithological data, consisting of 18,989 specimens belonging to Corvidae species collected between 2013 and 2022: every year from May to November birds are captured or shot and tested for WNV genome presence. We found that the incidence peaks between mid-July and late August, infected corvids seem on average 17% more likely to be captured with respect to susceptible ones and seroprevalence was estimated to be larger than other years at the end of 2018, consistent with the anomalous number of recorded human infections. Thanks to our modelling study we quantified WNV infection dynamics in the corvid community, which is still poorly investigated despite its importance for the virus circulation. To the best of our knowledge, this is among the first studies providing quantitative information on infection and immunity in the bird population, yielding new important insights on WNV transmission dynamics.
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Journal articleWarder SC, Piggott MD, 2025,
The future of offshore wind power production: Wake and climate impacts
, APPLIED ENERGY, Vol: 380, ISSN: 0306-2619 -
Journal articledo Prado AH, Mair D, Garefalakis P, et al., 2025,
The influence of grain size sorting on the roughness parametrization of gravel riverbeds
, Geomorphology, Vol: 471, ISSN: 0169-555XGrain size and surface roughness play crucial roles in modelling flow resistance and boundary shear stress in fluvial systems. However, the impact of grain size sorting on surface roughness, particularly for gravel-bed rivers composed of poorly-sorted sediments, has yet to be elucidated. Here we utilize a stochastic model to simulate generic riverbed surfaces, investigating the influence of sediment sorting on roughness. Through comparison with field-acquired data, we explore the relationships between grain size, sorting, presence of textural patches, and local roughness. Our analysis reveals significant spatial roughness variations on surfaces with poorer sorting conditions, driven by stochastic grain arrangements. Notably, surfaces with poorly sorted grains exhibit meter-scale patches, each with distinct roughness attributes. Consequently, upon characterizing the roughness of riverbeds made up of m-scale gravel bars, the sorting of the grains needs to be considered to account for the complexity of the relationships between water flow and riverbed.
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Journal articleMohammadpour A, Paluszny A, Zimmerman RW, 2025,
A robust 3D finite element framework for monolithically coupled thermo-hydro-mechanical analysis of fracture growth with frictional contact in porous media
, COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, Vol: 434, ISSN: 0045-7825 -
Journal articlePang B, Cheng S, Huang Y, et al., 2025,
Fire-Image-DenseNet (FIDN) for predicting wildfire burnt area using remote sensing data
, Computers and Geosciences, Vol: 195, ISSN: 0098-3004Predicting the extent of massive wildfires once ignited is essential to reduce the subsequent socioeconomic losses and environmental damage, but challenging because of the complexity of fire behavior. Existing physics-based models are limited in predicting large or long-duration wildfire events. Here, we develop a deep-learning-based predictive model, Fire-Image-DenseNet (FIDN), that uses spatial features derived from both near real-time and reanalysis data on the environmental and meteorological drivers of wildfire. We trained and tested this model using more than 300 individual wildfires that occurred between 2012 and 2019 in the western US. In contrast to existing models, the performance of FIDN does not degrade with fire size or duration. Furthermore, it predicts final burnt area accurately even in very heterogeneous landscapes in terms of fuel density and flammability. The FIDN model showed higher accuracy, with a mean squared error (MSE) about 82% and 67% lower than those of the predictive models based on cellular automata (CA) and the minimum travel time (MTT) approaches, respectively. Its structural similarity index measure (SSIM) averages 97%, outperforming the CA and FlamMap MTT models by 6% and 2%, respectively. Additionally, FIDN is approximately three orders of magnitude faster than both CA and MTT models. The enhanced computational efficiency and accuracy advancements offer vital insights for strategic planning and resource allocation for firefighting operations.
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Journal articleBeevers S, 2025,
Climate change policies reduce air pollution and increase physical activity:Benefits, costs, inequalities, and indoor exposures
, Environment International, ISSN: 0160-4120 -
Journal articleWariri O, Utazi CE, Okomo U, et al., 2025,
Multi-level determinants of timely routine childhood vaccinations in The Gambia: Findings from a nationwide analysis
, Vaccine, Vol: 43, ISSN: 0264-410XIntroduction: Achieving the ambitious goals of the Immunisation Agenda 2030 (IA2030) requires a deeper understanding of factors influencing under-vaccination, including timely vaccination. This study investigates the demand- and supply-side determinants influencing the timely uptake of key childhood vaccines scheduled throughout the first year of life in The Gambia. Methods: We used two nationally-representative datasets: the 2019–20 Gambian Demographic and Health Survey and the 2019 national immunisation facility mapping. Using Bayesian multi-level binary logistic regression models, we identified key factors significantly associated with timely vaccination for five key vaccines: birth dose of hepatitis-B (HepB0), first, second, and third doses of the pentavalent vaccine (Penta1, Penta2, Penta3), and first-dose of measles-containing vaccine (MCV1) in children aged 12–35 months. We report the adjusted Odds Ratios (aORs) and 95 % Credible Intervals (95 % CIs) in each case. Results: We found that demand-side factors, such as ethnicity, household wealth status, maternal education, maternal parity, and the duration of the household's residency in its current location, were the most common drivers of timely childhood vaccination. However, supply-side factors such as travel time to the nearest immunisation clinic, availability of cold-storage and staffing numbers in the nearest immunisation clinic were also significant determinants. Furthermore, the determinants varied across specific vaccines and the timing of doses. For example, delivery in a health facility (aOR = 1.58, 95 %CI: 1.02–2.53), living less than 30 min (aOR = 2.11, 95 %CI: 1.2–8.84) and living between 30 and 60 min (aOR = 3.68, 95 %CI: 1.1–14.99) from a fixed-immunisation clinic was associated with timely HepB0, a time-sensitive vaccine that must be administered within 24 h of birth. On the other hand, children who received Penta1 and Penta2 on time were three- to five-fold more
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Journal articleStocker B, Dong N, Perkowski EA, et al., 2025,
Empirical evidence and theoretical understanding ofecosystem carbon and nitrogen cycle interactions
, New Phytologist, Vol: 245, Pages: 49-68, ISSN: 0028-646XInteractions between carbon (C) and nitrogen (N) cycles in terrestrial ecosystems are simulated in advanced vegetation models, yet methodologies vary widely, leading to divergent simulations of past land C balance trends. This underscores the need to reassess our understanding of ecosystem processes, given recent theoretical advancements and empirical data. We review current knowledge, emphasising evidence from experiments and trait data compilations for vegetation responses to CO2 and N input, alongside theoretical and ecological principles for modelling. N fertilisation increases leaf N content but inconsistently enhances leaf-level photosynthetic capacity. Whole-plant responses include increased leaf area and biomass, with reduced root allocation and increased aboveground biomass. Elevated atmospheric CO2 also boosts leaf area and biomass but intensifies belowground allocation, depleting soil N and likely reducing N losses. Global leaf traits data confirm these findings, indicating that soil N availability influences leaf N content more than photosynthetic capacity. A demonstration model based on the functional balance hypothesis accurately predicts responses to N and CO2 fertilisation on tissue allocation, growth and biomass, offering a path to reduce uncertainty in global C cycle projections.
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Journal articleBenmoufok EF, Warder SC, Zhu E, et al., 2024,
Improving wind power modelling through granular spatial and temporal bias correction of reanalysis data
, ENERGY, Vol: 313, ISSN: 0360-5442 -
Journal articleLi H-Y, Lawrence JA, Mason PJ, et al., 2024,
Fast dynamic time warping and hierarchical clustering with multi-spectral and synthetic aperture radar temporal analysis for unsupervised winter food crop mapping
, Agriculture, ISSN: 2077-0472
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