The potential for kelp manufacture to lead to arsenic pollution of remote Scottish islands

Burning seaweed to produce kelp, valued for its high potash and soda content, was formerly a significant industry in remote coastal areas of Scotland and elsewhere. Given the high concentrations of arsenic in seaweeds, up to 100 mg kg(-1), this study investigates the possibility that the kelp industry caused arsenic contamination of these pristine environments. A series of laboratory-scale seaweed burning experiments was conducted, and analysis of the products using HPLC ICP-MS shows that at least 40% of the arsenic originally in the seaweed could have been released into the fumes. The hypothesis that the burning process transforms arsenic from low toxicity arsenosugars in the original seaweeds (Fucus vesiculosus and Laminaria digitata) to highly toxic inorganic forms, predominantly arsenate, is consistent with As speciation analysis results. A field study conducted on Westray, Orkney, once a major centre for kelp production, shows that elevated arsenic levels (10.7+/-3.0 mg kg(-1), compared to background levels of 1.7+/-0.2 mg kg(-1)) persist in soils in the immediate vicinity of the kelp burning pits. A model combining results from the burning experiments with data from historical records demonstrates the potential for arsenic deposition of 47 g ha(-1) year(-1) on land adjacent to the main kelp burning location on Westray, and for arsenic concentrations exceeding current UK soil guideline values during the 50 year period of peak kelp production.

Modeling continuous self-report measures of perceived emotion using generalized additive mixed models

Emotion research has long been dominated by the “standard method” of displaying posed or acted static images of facial expressions of emotion. While this method has been useful it is unable to investigate the dynamic nature of emotion expression. Although continuous self-report traces have enabled the measurement of dynamic expressions of emotion, a consensus has not been reached on the correct statistical techniques that permit inferences to be made with such measures. We propose Generalized Additive Models and Generalized Additive Mixed Models as techniques that can account for the dynamic nature of such continuous measures. These models allow us to hold constant shared components of responses that are due to perceived emotion across time, while enabling inference concerning linear differences between groups. The mixed model GAMM approach is preferred as it can account for autocorrelation in time series data and allows emotion decoding participants to be modelled as random effects. To increase confidence in linear differences we assess the methods that address interactions between categorical variables and dynamic changes over time. In addition we provide comments on the use of Generalized Additive Models to assess the effect size of shared perceived emotion and discuss sample sizes. Finally we address additional uses, the inference of feature detection, continuous variable interactions, and measurement of ambiguity.

Maintaining design intent for aircraft manufacture

Design and manufacture of aircraft requires deep multi-disciplinary understanding of system behaviour. The intention of the designer can get lost due to the many changes occurring to the product and the inability of the methods and tools used to capture it. Systems engineering and optimisation tools underpin industrial approaches to design, but are not without issue. The challenge is to find a route from concept to manufacture which enables designers to maintain their original intent. The novelty in this work is that the parameterisation used to build the CAD model reflects the manufacturing capability, ensuring design intent is maintained from concept to manufacture.

US Patent: Composite concrete article and method of manufacture thereof

A method of manufacturing a composite concrete article comprising affixing at least one layer of textile to a base layer and incorporating the base layer into a body of wet uncured concrete such that the base layer becomes embedded in the concrete, whereby at least a portion of the at least one textile layer defines at least a portion of a surface of the cured concrete article with the base layer embedded within the concrete to anchor the textile layer to the concrete.

The Application of Computational Chemistry and Chemometrics to Developing a Method for Online Monitoring of Polymer Degradation in the Manufacture of Bioresorbable Medical Implants

Bioresorbable polymers such as PLA have an important role to play in the development of temporary implantable medical devices with significant benefits over traditional therapies. However, development of new devices is hindered by high manufacturing costs associated with difficulties in processing the material. A major problem is the lack of insight on material degradation during processing. In this work, a method of quantifying degradation of PLA using IR spectroscopy coupled with computational chemistry and chemometric modeling is examined. It is shown that the method can predict the quantity of degradation products in solid-state samples with reasonably good accuracy, indicating the potential to adapt the method to developing an on-line sensor for monitoring PLA degradation in real-time during processing.