The growth and development of British underground and alternative comics, 1966–1986

Initially the terms 'underground' and 'alternative' are defined British underground periodicals and comics displayed a distinct influence from American underground publications, both in terms of their political ideas and their visual styles. After 1973 a less politically motivated form of alternative comic developed. The comparative financial failure of the majority of these comics is discussed. It can be said that these comics reflect their ideas and meanings through their drawing styles as well as their obvious political and social content. A wide range of comics is then examined in terms of their construction and underlying narrative structure, using a series of empirical tests devised for this purpose. The aim of these tests is to see if underground or alternative comics can be distinguished from mainstream comics by their form and structure rather than just their content. The influence of alternative comics can be felt both in the growing sophistication of mainstream British comics and in the reuse of comic imagery in graphic design and advertising

Data_Sheet_1_River reach-level machine learning estimation of nutrient concentrations in Great Britain.pdf

Nitrogen (N) and phosphorus (P) are essential nutrients necessary for plant growth and support life in aquatic ecosystems. However, excessive N and P can lead to algal blooms that deplete oxygen and lead to fish death and the release of toxins that are harmful to humans. Estimates of N and P levels in rivers are typically calculated at station or grid (>1 km) scale; therefore, it is difficult to visualise the evolution of water quality as water travels downstream. Using a high-resolution reach-scale river network and associating each reach with land cover fractions and catchment descriptors, we trained random forest models on aggregated data (2010–2020) from the Environmental Agency Open Water Quality Data Archive for 2,343 stations to predict long-term nitrate and orthophosphate concentrations at each river reach in Great Britain (GB). We separated the model training and predictions for different seasons to investigate the potential difference in feature importance. Our model predicted concentrations with an average testing coefficient of determination (R2) of 0.71 for nitrate and 0.58 for orthophosphate using 5-fold cross-validation. Our model showed slightly better performance for higher Strahler stream orders, highlighting the challenges of making predictions in small streams. Our results revealed that arable and horticultural land use is the strongest and most reliable predictor for nitrate, while floodplain extents and standard percentage runoff are stronger predictors for orthophosphate. Nationally, higher orthophosphate concentrations were observed in urbanised areas. This study shows how combining a river network model with machine learning can easily provide a river network understanding of the spatial distribution of water quality levels.</p