Treatment of estrogens and androgens in dairy wastewater by a constructed wetland system.

Constructed wetland systems (CWS) have been used as a low cost bio-filtration system to treat farm wastewater. While studies have shown that CWS are efficient in removing organic compounds and pathogens, there is limited data on the presence of hormones in this type of treatment system. The objective of this study was to evaluate the ability of the CWS to reduce estrogenic and androgenic hormone concentration in dairy wastewater. This was achieved through a year long study on dairy wastewater samples obtained froma surface flow CWS. Analysis of hormonal levels was performed using a solid phase extraction (SPE) sample clean-up method, combined with reporter gene assays (RGAs) which incorporate relevant receptors capable of measuring total estrogenic or androgenic concentrations as low as 0.24 ng L1 and 6.9 ng L1 respectively. Monthly analysis showed a mean removal efficiency for estrogens of 95.2%, corresponding to an average residual concentration of 3.2 ng L1 17b-estradiol equivalent (EEQ), below the proposed lowest observable effect concentration (LOEC) of 10 ng L1. However, for one month a peak EEQ concentration of 115 ng L1 was only reduced to 18.8 ng L1. The mean androgenic activity peaked at 360 ng L1 and a removal efficiency of 92.1% left an average residual concentration of 32.3 ng L1 testosterone equivalent (TEQ). The results obtained demonstrate that this type of CWS is an efficient system for the treatment of hormones in dairy wastewater. However, additional design improvements may be required to further enhance removal efficiency of peak hormone concentrations.

Computational techniques for a simple theory of conditional preferences

A simple logic of conditional preferences is defined, with a language that allows the compact representation of certain kinds of conditional preference statements, a semantics and a proof theory. CP-nets and TCP-nets can be mapped into this logic, and the semantics and proof theory generalise those of CP-nets and TCP-nets. The system can also express preferences of a lexicographic kind. The paper derives various sufficient conditions for a set of conditional preferences to be consistent, along with algorithmic techniques for checking such conditions and hence confirming consistency. These techniques can also be used for totally ordering outcomes in a way that is consistent with the set of preferences, and they are further developed to give an approach to the problem of constrained optimisation for conditional preferences.

Segregation Preferences of Urban and Rural 16-year-olds in Northern Ireland

Sectarian violence in the Northern Ireland is often perceived to be mostly confined to cities. The aim of this paper is to explore statistically what factors contribute to segregation preferences among young people living in rural and urban areas, using the 2005–2009 Young Life and Times (YLT) survey – an annual attitudes survey of 16-year-olds. The findings show that religious and national identities are the strongest predictors of segregation preferences among 16-year-olds, regardless of where they live and what background they have. Those living in rural areas of Northern Ireland are more supportive of residential, workplace and educational segregation than those living in more urban areas. This research highlights the need for government policy to take rurality into account. Nevertheless, some variables significantly determine segregation preferences regardless of where respondents live, such as attendance of segregated schools, being female, or strength of national and religious identity. Consequently, policy initiatives should continue to address the effect of segregation, especially in relation to education, and future research exploring social class and gender is recommended. In conclusion, the perception of the violent ‘urban spaces’ and the ‘peaceful countryside’ has to be challenged.

A graded BDI agent model to represent and reason about preferences

In this research note, we introduce a graded BDI agent development framework, g-BDI for short, that allows to build agents as multi-context systems that reason about three fundamental and graded mental attitudes (i.e. beliefs, desires and intentions). We propose a sound and complete logical framework for them and some logical extensions to accommodate slightly different views on desires. © 2011 Elsevier B.V. All rights reserved.