Search

Compositional analysis has proved to be a powerful tool for investigating the recycling of transparent glass in the Roman and Byzantine periods. This paper expands the focus further to explore the recycling of coloured and opaque glass in the Late Iron Age and Roman periods in Britain. A review of the findings to date, predominantly for beads and bangles, illustrates how a combination of methods is needed to identify when coloured glass has been recycled and altered in the past. Chemical analysis, microstructural examination and experimentation have been used to understand how coloured glass was mixed, diluted, and modified with novel colourants and opacifiers, in ways that are not apparent from non‐destructive chemical analysis alone. The paper discusses the possible types and sources of trade glass, the challenges of recycling mono‐ and polychrome glass in different colours, and the unconventional methods used by glassworkers on the periphery of Roman influence to extend and modify coloured glass to produce distinctive items in order to express their identity. [ABSTRACT FROM AUTHOR]

Climate change impact assessments require detailed information regarding the impact of weather in the current climate. This study focuses on the road network, using UK Highways Agency Traffic Information System data and NIMROD precipitation radar data to determine the impact of precipitation on traffic speeds along the M1 and M6 corridors for use in future impact assessments. The results show that whilst precipitation does impact on traffic speeds, there is no universal significant single factor relationship between the two variables and only a small amount of variance in traffic speeds can be explained by the intensity of precipitation. However, a threshold appears to exist simply at 0 mm h−1 (i.e. precipitation/no precipitation). The significance of this threshold is tested in this paper, but it appears clear that a universal relationship between precipitation intensity and vehicle speeds is unlikely to exist and local conditions such as capacity and drainage are equally as important to take into account. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Many impactful weather and climate events include two or more variables (like temperature or precipitation) having high or low values (e.g., hot dry summers). Understanding biases in the relationship between modelled variables is important for characterizing uncertainties in the risks associated with compound events. We present a framework for evaluating the relationships between different variables (multi‐variate bias). We illustrate our approach with UK temperature and precipitation, using HadUK‐Grid observations and two model ensembles (12 and 2.2 km horizontal resolution) of the HadGEM3 regional model used in UK Climate Projections both forced with the same driving conditions. There are distinct regional patterns in the biases of both the Pearson correlation coefficients and coefficients of linear regression between temperature and precipitation in both resolutions, for example, large areas of positive biases in the Pearson correlation coefficients across the United Kingdom in winter, and negative biases across most of England in summer. We combine the Pearson correlation coefficients and bias in the coefficient of linear regression into a combined metric and consider regions where either the bias in the coefficient of linear regression or the bias in Pearson correlation coefficient is significantly dominant over the other. By considering only days with similar North Atlantic driving conditions using Met Office Weather Patterns we can identify regions with significant differences between the two model resolutions that are attributable to the difference in model resolution and structural design. The root mean square error (RMSE) of correlation bias across the United Kingdom is reduced in the 2.2 km compared to the 12 km model data in each season except summer where it is broadly similar. For Weather Pattern 2 (North Atlantic Oscillation positive) days the RMSE for correlation coefficient and the coefficient of linear regression is twice as large than for all conditions. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence will transform clinical practice over the next decade and the early impact of this will likely be the integration of image analysis and machine learning into routine histopathology. In the UK and around the world, a digital revolution is transforming the reporting practice of diagnostic histopathology and this has sparked a proliferation of image analysis software tools. While this is an exciting development that could discover novel predictive clinical information and potentially address international pathology workforce shortages, there is a clear need for a robust and evidence‐based framework in which to develop these new tools in a collaborative manner that meets regulatory approval. With these issues in mind, the NCRI Cellular Molecular Pathology (CM‐Path) initiative and the British In Vitro Diagnostics Association (BIVDA) have set out a roadmap to help academia, industry, and clinicians develop new software tools to the point of approved clinical use. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]