Presence and potential impact of anthropogenic nesting materials on a colonial breeding waterbird

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 964, P. 178588 - 178588

Published: Jan. 24, 2025

Despite the vital importance of wetlands globally, these habitats have increasingly received anthropogenic materials, such as plastics, which can impact wildlife support. commonly found in nests Eurasian spoonbill (Platalea leucorodia), presence materials has never been quantified. Here, we monitored occurrence nesting (ANM) Camargue wetland Southern France during two breeding seasons (2021-2022). We also investigated their potential function by associating with breeder experience (based on age) and hatching success. Out 439 nests, 39 % contained at least one material item, usually plastic sheet-like shape white-transparent colours. Throughout season, proportion ANM nest lining increased, coinciding a decrease natural (i.e. vegetation). A higher green was detected egg phase compared to other phases development. Our results did not support correlation between experience, nor Further studies (e.g., physiological through necropsy or fine temperature assessment) may disentangle benefits adverse effects waterbird

Language: Английский

Prediction of porosity, hardness and surface roughness in additive manufactured AlSi10Mg samples

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(3), P. e0316600 - e0316600

Published: March 10, 2025

Despite the advantages of additive manufacturing, its widespread adoption is still hindered by poor quality fabricated parts. Advanced machine learning techniques to predict part can improve repeatability and open manufacturing various industries. This study aims accurately relative density, surface roughness hardness AlSi10Mg samples produced selective laser melting regarding process parameters such as scan speed, layer thickness, power, hatch distance. For this purpose, data including porosity, hardness, were extracted from literature, additional measurements performed on manufactured in current work. work compares five supervised algorithms, artificial neural networks, support vector regression, kernel ridge random forest, Lasso regression. These models are evaluated based coefficient determination mean squared error. On basis computational results, network outperformed predicting roughness, hardness. Feature importance analysis compiled dataset using ANN revealed that power speed most important features affecting density (e.g., porosity) while thickness significantly impact The identified an optimal region achieves a > 99%, < 10 µm, 120 HV. results presented provide significant for potentially reducing experimentation costs identifying optimize

Language: Английский